Data Scientist Jobs in the UAE

Data scientistInternet City, DubaiDuration:12 monthsVisa : Work Permit, Tourist Visa, Dependent VisaNeed Someone in Dubai only.Responsibilities• Develop and fine-tune computer-vision models for object detection, tracking, and segmentation using PyTorch / TensorFlow and OpenCV.• Work hands-on with modern architectures including YOLO, RF-DETR, and Vision-Language Models (VLMs), fine-tuning them for target use cases.• Curate, preprocess, and augment image and video datasets, maintaining a tight feedback loop with the annotation team.• Train, tune, and evaluate models against rigorous metrics (mAP, precision, recall) and iterate to meet accuracy targets.• Track experiments and results using tools such as MLflow or Weights & Biases.• Apply model-optimisation techniques (TensorRT, ONNX) with real-world deployment constraints in mind.Qualifications• Bachelors or Masters degree in Computer Science, Data Science, Machine Learning, or a related field.• 3 – 7 years of applied machine-learning experience, with a focus on computer vision.• Strong Python skills and hands-on experience with PyTorch or TensorFlow and OpenCV.• Demonstrated experience with detection, tracking, and segmentation models (e.g. YOLO, RF-DETR).• Experience fine-tuning Vision-Language Models is an advantage.• Familiarity with experiment-tracking and model-optimisation

Senior Data Science Manager45-50k AED per month plus benefitsAbu DhabiDiscover the OpportunitiesWe are hiring a Senior Data Science Manager to join a leading financial institution in Abu Dhabi. Our client works across diverse banking domains to leverage data assets and implement cutting edge technology solutions. As a key leader in the business services division, you will bridge the gap between technical teams and senior business stakeholders, translating complex organisational challenges into high impact machine learning solutions that drive measurable growth.Discover the roleIn this position you will lead the development and adoption of data science and artificial intelligence initiatives across critical banking sectors like retail, risk, credit, and operations. You will provide strategic and technical leadership to a dedicated team, ensuring that all models are robust, explainable, and ready for production. This role focuses on embedding advanced statistical insights directly into the core decision making processes of the business.Discover the responsibilitiesDesign and deploy advanced machine learning models to improve efficiency and risk outcomes across customer journeysApply supervised, unsupervised, and time series modeling techniques using structured and semi structured dataLead feature engineering, model selection, training, validation, and benchmarkingConduct experimentation using hypothesis testing and statistical inference to quantify model impactCollaborate with engineering teams to ensure models are production ready and maintained through standard continuous integration practicesMonitor model performance, stability, and data drift to ensure ongoing reliability and regulatory complianceDrive the adoption of data driven practices across business units by managing change initiatives and training programsManage and mentor a team of two to five direct reports, organising activities to deliver on strategic objectivesDiscover the requirementsMinimum 6 years of experience in data science with proven expertise in artificial intelligence and cloud based deploymentsStrong background in banking processes, operations, terminologies, and machine learning operationsExperience leading cross functional teams and agile squads in a regulated environmentBachelor degree in a quantitative field such as Computer Science, Artificial Intelligence, Statistics, or MathematicsMaster degree in a related field is highly preferredAdvanced technical skills in Python, generative AI, large language models, and cloud based pipelines using Azure or AWS

Role: Senior Data ScientistLocation: DubaiJob DescriptionSTYLI is an e-commerce marketplace founded in 2019 by Landmark Group, emerging as one of the largest fashion and beauty platforms in the GCC and India. With a strong focus on trendy, affordable fashion and beauty products, STYLI brings over 40,000 styles to men, women, kids, and beauty enthusiasts, offering them the latest global trends delivered directly to their doorsteps.Our vision is to be the most aspirational value fast fashion and lifestyle destination, delivering seamless service excellence. We aim to create personalized experiences, engaging customers across all touchpoints, and continually expanding our curated selection to meet their evolving needs. STYLI has quickly become a leading player in the e-commerce fashion space across the GCC - Saudi Arabia, UAE, Bahrain, and Kuwait and in India.Role overviewWe are looking for a highly motivated, Senior Data Scientist who enjoys solving complex and ambiguous problems using data, statistics and machine learning. You will independently own high-impact Data Science initiatives from problem definition through modelling, experimentation, productionization and impact measurement. You will work closely with Product, Engineering and business stakeholders, while also contributing technical guidance and supporting the development of the wider Data Science team.Key responsibilities● Independently solve complex business and product problems using data, statistics and machine learning.● Own Data Science projects from problem framing and data exploration through modelling, experimentation, deployment and impact measurement.● Extract, clean and analyze large datasets, and design reliable features, training datasets and evaluation frameworks.● Build, evaluate, tune and improve machine-learning, statistical or optimization models using appropriate baselines and metrics.● Conduct detailed error analysis and identify issues related to bias, leakage, data quality and model robustness.● Design and analyze experiments and other measurement approaches to estimate incremental business impact.● Work closely with Product, Engineering and business teams to integrate solutions into production workflows.● Monitor model quality, data health, operational performance and business outcomes after launch.● Make practical trade-offs across accuracy, interpretability, latency, scalability, maintainability and cost.● Write maintainable, reusable, version-controlled and well-documented code.● Communicate findings, recommendations, assumptions and risks clearly to technical and non-technical stakeholders.● Contribute to technical discussions, code and model reviews, and support other Data Scientists when needed. Data Science & AIMust-have skills and experience● Advanced proficiency in Python and SQL, with strong experience building maintainable, reusable and production-oriented Data Science code.● Strong practical experience building, evaluating and deploying machine-learning models, with depth in model selection, experimentation, error analysis and business-impact measurement.● Strong understanding of probability, statistical inference, experimental design and common sources of bias, confounding and data leakage.● Demonstrated experience independently owning multiple Data Science initiatives from problem framing through production and post-launch monitoring.● Experience designing reliable training and evaluation datasets and identifying upstream data-quality or instrumentation gaps.● Working knowledge of production ML concepts, including batch or real-time inference, APIs, CI/CD, model monitoring and retraining.● Ability to make sound technical trade-offs across model quality, latency, scalability, maintainability and infrastructure cost.● Strong stakeholder-management and communication skills, with the ability to influence product and business direction through data and deliver measurable customer or business impact.● A proactive, outcome-oriented mindset with the ability to operate effectively under ambiguity, take ownership without close supervision, and provide constructive technical guidance to others.Good-to-have skills● Strong experience in one or more areas such as recommendation systems, search and ranking, forecasting, pricing, optimization, customer modelling, causal inference, computer vision or generative AI.● Familiarity with distributed processing, orchestration, backend APIs, cloud deployment, search or vector databases, caching technologies and model-serving systems.● Experience in e-commerce, retail, fashion or another large-scale digital product environment.Education and experience● Bachelor's, Master's or PhD in Computer Science, Data Science, Statistics, Mathematics, Engineering, Economics, Operations Research or a related quantitative field.● Typically 5+ years of relevant Data Science or applied machine-learning experience; equivalent depth, scope and demonstrated impact will also be considered.What success looks likeA successful Senior Data Scientist will independently solve complex problems, apply strong technical judgement, deliver reliable production solutions, influence cross-functional decisions and create measurable customer or business impact. Data Science & AI

Since launching in Kuwait in 2004, talabat, the leading on-demand food and Q-commerce app for everyday deliveries, has been offering convenience and reliability to its customers. talabat’s local roots run deep, offering a real understanding of the needs of the communities we serve in eight countries across the region.We harness innovative technology and knowledge to simplify everyday life for our customers, optimize operations for our restaurants and local shops, and provide our riders with reliable earning opportunities daily.Here at talabat, we are building a high performance culture through engaged workforce and growing talent density. We're all about keeping it real and making a difference. Our 6,000+ strong talabaty are on an awesome mission to spread positive vibes. We are proud to be a multi great place to work award winner.Job DescriptionAbout the Applied AI TribeThe Applied AI Tribe is talabat's internal AI engine, a team obsessed with turning cutting-edge AI into genuine, measurable business value. We don't build AI for its own sake. Our north star is Net AI Value Delivered.We have shipped 40+ AI products across four strategic streams: Product & Tech Platform - AI capabilities embedded into talabat's core product and engineering platform. Business Automations - streamlined workflows that save real time and money across operations. Self-Serve AI Tools - empowering talabatis to build and use AI without needing to raise a ticket. Training & Onboarding - upskilling the tribe so every team can participate in the AI era.Our engineering approach is built around shipping agentic AI systems at scale, with a harness-first philosophy, evaluation embedded into every product iteration, and production feedback loops that drive the roadmap.The RoleAs Manager, Data Scientist - AI within the Applied AI Tribe, you will lead a team of data scientists building and shipping the ML and generative AI systems that power decisions across talabat's product and business. You are equally a people leader and a technical practitioner, close enough to the work to make great technical decisions and grow your team, while owning the roadmap and delivery.Your team's work spans the full AI lifecycle: from framing ambiguous business problems through data modelling, feature engineering, model training, deployment, and production monitoring. A significant focus will be leveraging LLMs, agentic systems, and generative AI to automate decisions, enrich data, and build intelligent experiences at scale, all measured against Net AI Value Delivered.What's On Your Plate?People Leadership: Lead, grow, and retain a team of data scientists, setting a high bar for technical quality, fostering a culture of ownership, and supporting each person's career development. Partner with recruiting to attract top data science and ML talent as the tribe scales. Mentor team members in ML best practices, agentic system design, production engineering, and stakeholder communication. Run effective team rituals, planning, design reviews, retrospectives, that keep the team aligned and moving with pace.Technical Strategy & Delivery: Translate ambiguous business problems into well-scoped ML and AI solutions with clear, measurable success criteria tied to the tribe's north star. Own the team's technical roadmap: prioritise high-impact work across data enrichment, business automation, self-serve tooling, and agentic product features. Champion harness-first thinking, ensure evaluation pipelines, LLM observability, tooling, and infrastructure are in place before agent logic is built on top. Embed evaluation into every product iteration: golden datasets, stakeholder-aligned metrics, and weekly eval jobs that guide what gets built, fixed, or retired. Oversee the full ML lifecycle: data pipelines, feature engineering, model training, production deployment, serving, and monitoring. Drive adoption of LLMs and generative AI for data enrichment, smart content understanding, and automated decision-making at scale. Design and analyse experiments (A/B and multivariate) to rigorously measure model and product impact. Elevate ML and engineering standards across the team, improving MLOps, code quality, tooling, and internal learning programmes.Cross-functional Partnership: Partner with product managers and business teams to identify high-value AI opportunities and shape them into the team's roadmap. Communicate clearly with senior stakeholders, from problem framing through to results and recommendations. Collaborate with engineering teams to understand data systems, build reliable data models, and ensure smooth production integration.QualificationsWhat Did We Order?Technical Experience Deep expertise in machine learning, generative AI, deep learning, NLP, recommendation systems, and data mining. Hands-on knowledge of ML and GenAI frameworks: Scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow, Transformers, and LLM fine-tuning. Proficiency with the OpenAI SDK and familiarity with major LLM provider APIs for building and orchestrating production AI systems. Hands-on experience with LangGraph for building stateful, multi-step agentic workflows and complex agent orchestration patterns. Experience with Hugging Face (Transformers, Hub, Inference API) for model sourcing, fine-tuning, and deployment. Solid understanding of embeddings, dense and sparse retrieval, vector databases, and semantic search, for building RAG pipelines and similarity-based applications. Strong software engineering fundamentals: clean production code, data structures and algorithms, ML system design. Proven experience shipping and monitoring ML models in production, with a solid grasp of MLOps practices. Strong data and ML engineering skills: building and orchestrating data pipelines (e.g., Airflow) and robust feature engineering. Excellent SQL and Python; solid statistical foundations including experiment design, causal inference, and predictive methods. Familiarity with agentic system design (harness-first thinking, LLM observability, evaluation pipelines, and production feedback loops) is a strong plus. Experience with BigQuery and Google Cloud Platform is a plus.Qualifications Bachelor's degree in Engineering, Computer Science, or a related field. A postgraduate degree is a plus. 6+ years of experience in data science, ML engineering, and/or generative AI, including shipping models to production. 2+ years managing or leading a data science or ML team, with a track record of developing talent and delivering through others. Experience building ML systems in an online consumer product environment is a strong plus.Additional InformationMindset & Ways of Working Curiosity over certainty: you're excited to try the new model or framework this week. Ownership: you treat cost, latency, and business impact as your problem, and your team's. Bias to build: a prototype in a real workflow beats a perfect design doc every time. Comfortable with ambiguity: most of what we build has no playbook yet, and you help write it. Keep it simple: pick the approach that delivers the most value with the least complexity, and #makeithappen.At talabat, your success is defined by both results and behaviors. We hire for excellence in craft and for the Leadership Principles that shape how we think, decide, and collaborate. What you achieve matters, and how you achieve it defines us. Together, they move the business forward and deliver great experiences. Learn more about our Leadership Principles here.

Principal Data Scientist – Generative AI & Agentic SystemsLocation: Dubai, UAEWe are looking for an exceptional Principal Data Scientist to lead the design and delivery of advanced AI solutions, with a particular focus on Generative AI, agentic systems and production-grade RAG architectures.This is a highly hands-on technical role. The successful candidate will remain close to the code while providing technical leadership, owning complex solution domains and translating real business problems into scalable AI products.What you'll be doing- Design and build complex agentic RAG systems, multi-step AI workflows and LLM-powered applications.- Lead AI solutions end-to-end, from experimentation and architecture through production deployment, monitoring and continuous improvement.- Write and review production-quality Python and modern AI/ML code.- Own solution architecture, technical standards and engineering best practices across AI initiatives.- Provide technical leadership and mentorship while remaining an active hands-on contributor.- Evaluate and apply emerging models, frameworks, vector databases, orchestration tools and AI engineering approaches.- Work closely with business stakeholders to ensure AI initiatives deliver measurable commercial and operational value.- Build robust data and model pipelines, testing, evaluation, observability and MLOps practices.What we're looking for- 8+ years of relevant experience across Data Science, Machine Learning, AI Engineering or closely related areas.- Strong recent experience building complex agentic RAG systems, beyond standard question-answering chatbots.- Deep practical knowledge of LLMs, retrieval, embeddings, vector databases, agent orchestration and production AI architectures.- Strong hands-on Python skills and current software engineering practices, including APIs, testing, version control, CI/CD and maintainable production code.- Demonstrated technical leadership, team leadership or ownership of a significant technical domain.- A track record of translating AI capabilities into measurable business outcomes.- Experience operating in demanding, technically sophisticated environments.- Broader experience across different organisations, products or business domains is highly valued.Nice to have- Experience productising AI and ML solutions within the Microsoft Azure ecosystem.- Cloud-native architecture and deployment experience.- Strong MLOps and data engineering exposure.- Experience taking Generative AI products from prototype through to reliable production use.This opportunity is particularly suited to a senior technical leader who still enjoys building, coding and solving difficult AI problems directly rather than operating primarily as a people or vendor manager.

Job PurposeAs a Data Scientist at SAAL.ai, you will apply machine learning and deep learning techniques to solve real-world business problems across numeric and text-based analytics. You will work across the full data science lifecycle—from data acquisition and feature engineering to model development and deployment—collaborating closely with engineering, product, and DevOps teams to deliver customer-centric AI solutions.Key ResponsibilitiesDevelop and deliver high-quality machine learning and deep learning models across a broad range of analytics use cases.Understand the practical scope and constraints of AI models within products and apply appropriate modeling techniques to ensure business relevance and technical robustness.Acquire data from diverse sources, performing data exploration and preprocessing on structured and unstructured datasets, conducting feature engineering, evaluating algorithms and architectures, and iteratively refining models to improve performance.Identify valuable data sources and automate data collection and preparation processes where possible.Build, fine-tune, and maintain machine learning pipelines, integrate algorithms and packages into the platform, and contribute new models to the platform marketplace.Collaborate closely with engineering and DevOps teams to support model deployment and operationalization.Contribute to applied research activities, propose data-driven solutions to business challenges, and work with product, support, and client-facing teams to ensure successful rollout of AI solutions into trials and general availability.Key SkillsStrong foundation in the theory and applied practice of machine learning and deep learning. Experience developing pipelines for structured and unstructured data.Hands-on experience with deep learning frameworks such as TensorFlow and PyTorch, and machine learning libraries such as scikit-learn.Proficiency in Python, with working knowledge of Java or R.Experience developing and exposing models through RESTful APIs and containerized deployments using Docker.Strong coding practices, including appropriate use of data structures and clean, maintainable code.Applied experience in NLP, including the use of transformer-based models and libraries such as Hugging Face, spaCy, or Gensim. Familiarity with SQL, Pandas, Apache Spark, and data processing at scale.Experience using ML lifecycle tools such as MLflow.Strong analytical thinking, communication skills, and the ability to present technical concepts clearly to internal and external stakeholders.Ability to work collaboratively across multidisciplinary teams.Desirable SkillsExperience working with large language models (LLMs), including offline deployment scenarios. Exposure to cloud-based environments (AWS, GCP, Azure). Experience with data visualization tools and collaborative environments such as Jupyter Notebooks and Git-based workflows. Domain experience with education or finance datasets is an advantage.

Job DescriptionJob Purpose:To lead the development and deployment of advanced machine learning solutions that power personalized customer decisioning, while driving best‑in‑class modeling, MLOps, and data engineering practices. This role combines hands‑on technical expertise with leadership to deliver scalable models, robust pipelines, and high‑impact insights across the organization.Modeling ExpertiseWhat You Will Bring:Advanced experience in building and deploying propensity models for cross-sell and upsell.Deep understanding of Next Best Action / Next Best Offer engines.Strong grasp of supervised learning, uplift modeling, and causal inference.Programming & EngineeringExpert-level coding in Python (mandatory); familiarity with R or Scala is a plus.Experience with distributed computing (Spark, Dask, Ray).Proficient in writing production-grade ML pipelines using tools like Airflow, MLflow, or Kubeflow.Strong understanding of software engineering best practices: version control, CI/CD, testing.Data InfrastructureExperience with cloud platforms (AWS/GCP/Azure) and data warehouses (Snowflake, BigQuery, Redshift).Strong SQL skills; ability to optimize queries and manage large datasets.MLOps & DeploymentExperience deploying models to production via APIs or streaming systems (Kafka, Flink).Proficient in model versioning, experiment tracking, and deployment using MLflow, SageMaker, or Vertex AI.Monitoring & ObservabilityAbility to set up model monitoring, drift detection, and alerting systems using Prometheus, Grafana, Evidently, or custom dashboards.Experience with logging frameworks and performance profiling for ML services.GenAI (Preferred)Experience with LLMs, embeddings, prompt engineering, and vector databases (e.g., FAISS, Pinecone).Ability to integrate GenAI into decisioning systems or customer-facing products.Leadership & CollaborationAbility to lead senior data scientists while remaining hands-on.Comfortable working with product managers, engineers, and business stakeholders.What You We Are Looking ForBachelors degree in a relevant field including Econometrics,Acturial Science.Maths/Statistics, Computer Science etc,10 years experience in a senior data scientist positionModelling experience including knowledge of Cloud and GenAIStrong Analytics experience in the bank sector is an advantageWhat’s In It For YouPay for performance culture (Competitive and performance-linked compensation)Diverse workforce and inclusive cultureCareer development and growth opportunities by designWork with the best minds in the fieldGet opportunities to bring your whole self to the organization and perform to your best.Job DetailsRole Level: Executive-Level Work Type: Full-Time Country: United Arab Emirates City: Dubai Company Website: http://www.rakbank.ae Job Function: Information Technology (IT) Company Industry/Sector: BankingWhat We OfferAbout The CompanySearching, interviewing and hiring are all part of the professional life. The TALENTMATE Portal idea is to fill and help professionals doing one of them by bringing together the requisites under One Roof. Whether you're hunting for your Next Job Opportunity or Looking for Potential Employers, we're here to lend you a Helping Hand.ReportDisclaimer: talentmate.com is only a platform to bring jobseekers & employers together. Applicants are advised to research the bonafides of the prospective employer independently. We do NOT endorse any requests for money payments and strictly advice against sharing personal or bank related information. We also recommend you visit Security Advice for more information. If you suspect any fraud or malpractice, email us at abuse@talentmate.com.

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Data Scientist based in United Arab Emirates.This is a senior, hands-on data science role focused on improving the accuracy and intelligence of a high-volume, real-time identification platform. You’ll work with complex, noisy, and often unlabeled data to develop machine learning approaches that distinguish browsers and devices with greater precision. The role combines advanced machine learning, statistical analysis, experimentation, and backend engineering in a highly technical environment. You’ll own projects from initial research and experimentation through production deployment and integration with real-time services. You’ll also help shape engineering practices, data-driven decision-making, and effective approaches to machine learning across the wider team. This is a fully remote opportunity suited to someone who enjoys solving challenging problems where research, software engineering, and applied ML meet.AccountabilitiesDevelop data-driven algorithms using raw, noisy, and unlabeled data to improve browser and device identification capabilities. Design and implement supervised, semi-supervised, and unsupervised machine learning approaches, including methods for high-cardinality categorical data. Own data science initiatives end to end, from problem definition and experimentation through production deployment and integration with real-time services. Design experiments and technical solutions for real-time inference, model-to-service integration, training automation, and other machine learning engineering challenges. Conduct exploratory data analysis to investigate business and technical questions, identify anomalies, and evaluate model and dataset performance. Develop practical approaches for collecting and evaluating data when labeled datasets are limited or unavailable. Contribute to an engineering-focused, data-driven culture by sharing tools, methodologies, and effective data science practices with colleagues. Collaborate across data science and engineering functions to turn machine learning concepts into reliable, production-ready services. Participate in a shared on-call rotation, with schedules communicated in advance and coverage designed to minimize disruption outside normal working hours. Requirements5+ years of professional experience spanning machine learning, data science, and backend development or closely related engineering disciplines. Advanced knowledge of machine learning fundamentals and statistical methodologies. Strong practical experience with supervised learning, including gradient boosting and approaches for high-cardinality categorical data. Hands-on experience with semi-supervised and unsupervised learning techniques. Strong exploratory data analysis and creative problem-solving skills, particularly when working with incomplete, noisy, or unlabeled datasets. Proven experience developing real-time machine learning services, including model inference and integration between models and production applications. Ability to transform machine learning models into minimum viable real-time web services and production-ready solutions. Excellent coding and software engineering skills, including strong SQL capabilities and familiarity with Git, CI/CD pipelines, IDEs, and shell scripting. Strong communication skills and fluent English, with the ability to collaborate effectively in a distributed international environment. A research mindset and academic background are advantageous. Experience with Go and backend development is a plus. Familiarity with analytical data platforms such as ClickHouse, Snowflake, or BigQuery is beneficial. Experience with data transformation frameworks such as dbt, visualization tools such as Superset, Tableau, or Looker, or vector databases such as Pinecone, FAISS, or Qdrant is a plus. Experience building embedding-based search systems is advantageous. Familiarity with technologies such as Go, SQL, advanced ML frameworks, ClickHouse, dbt, and AWS is beneficial. BenefitsCompetitive compensation, with the source role indicating a US cash compensation range of $152,000–$205,000 USD; compensation ranges are location-specific and may differ for candidates based in India. Fully remote working environment. Opportunity to work on challenging machine learning problems involving real-time systems, large-scale data, and advanced device intelligence. Exposure to modern machine learning, data engineering, backend development, and cloud infrastructure technologies. Opportunity to contribute to technical strategy and influence engineering and data science practices. Collaboration with a globally distributed team. Inclusive environment that values diverse experiences, perspectives, and backgrounds. Candidates must be authorized to work from their home location; visa sponsorship is not provided. Participation in a shared on-call rotation with advance scheduling and an effort to balance coverage fairly while minimizing off-hours disruption. How Jobgether WorksWe use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.We appreciate your interest and wish you the best! Why Apply Through Jobgether?Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

Job Purpose JOB DESCRIPTION To lead the development and deployment of advanced machine learning solutions that power personalized customer decisioning, while driving best‑in‑class modeling, MLOps, and data engineering practices. This role combines hands‑on technical expertise with leadership to deliver scalable models, robust pipelines, and high‑impact insights across the organization.Modeling ExpertiseWhat You Will Bring:Advanced experience in building and deploying propensity models for cross-sell and upsell.Deep understanding of Next Best Action / Next Best Offer engines.Strong grasp of supervised learning, uplift modeling, and causal inference.Programming & EngineeringExpert-level coding in Python (mandatory); familiarity with R or Scala is a plus.Experience with distributed computing (Spark, Dask, Ray).Proficient in writing production-grade ML pipelines using tools like Airflow, MLflow, or Kubeflow.Strong understanding of software engineering best practices: version control, CI/CD, testing.Data InfrastructureExperience with cloud platforms (AWS/GCP/Azure) and data warehouses (Snowflake, BigQuery, Redshift).Strong SQL skills; ability to optimize queries and manage large datasets.MLOps & DeploymentExperience deploying models to production via APIs or streaming systems (Kafka, Flink).Proficient in model versioning, experiment tracking, and deployment using MLflow, SageMaker, or Vertex AI.Monitoring & ObservabilityAbility to set up model monitoring, drift detection, and alerting systems using Prometheus, Grafana, Evidently, or custom dashboards.Experience with logging frameworks and performance profiling for ML services.GenAI (Preferred)Experience with LLMs, embeddings, prompt engineering, and vector databases (e.g., FAISS, Pinecone).Ability to integrate GenAI into decisioning systems or customer-facing products.Leadership & CollaborationAbility to lead senior data scientists while remaining hands-on.Comfortable working with product managers, engineers, and business stakeholders.What You We Are Looking ForBachelor's degree in a relevant field including Econometrics,Acturial Science.Maths/Statistics, Computer Science etc,10 years' experience in a senior data scientist positionModelling experience including knowledge of Cloud and GenAIStrong Analytics experience in the bank sector is an advantageWhat’s In It For YouPay for performance culture (Competitive and performance-linked compensation)Diverse workforce and inclusive cultureCareer development and growth opportunities by designWork with the best minds in the fieldGet opportunities to bring your whole self to the organization and perform to your best.

Data ScientistAbout The RoleWe are seeking an experienced Data Scientist to design, develop, and deploy advanced machine learning and artificial intelligence solutions that address complex business challenges. The successful candidate will work across the full data science lifecycle, leveraging structured and unstructured data to build scalable, production-ready models and deliver measurable business value.Working closely with Engineering, Product, and DevOps teams, you will contribute to the development of innovative AI-driven products and solutions, ensuring best practices in model development, deployment, and operationalization.Key ResponsibilitiesDesign, develop, and deploy machine learning and deep learning models to solve business and operational challenges. Apply advanced statistical, machine learning, and AI techniques to extract insights from structured and unstructured datasets. Perform data acquisition, exploration, cleansing, preprocessing, and feature engineering activities. Evaluate and select appropriate algorithms, architectures, and modelling approaches based on business requirements. Build, optimize, and maintain end-to-end machine learning pipelines. Fine-tune and enhance model performance through experimentation, testing, and continuous improvement. Identify new data sources and automate data collection and preparation processes where possible. Develop solutions across predictive analytics, natural language processing (NLP), and AI-driven automation use cases. Integrate machine learning models into production environments using APIs and containerized deployment approaches. Collaborate with engineering and DevOps teams to support model deployment, monitoring, and operational excellence. Contribute to research and innovation initiatives, evaluating emerging AI and machine learning technologies. Work closely with stakeholders to translate business requirements into scalable data science solutions. Present insights and technical recommendations to both technical and non-technical audiences. Support solution rollout, user adoption, and ongoing optimization activities. Required QualificationsBachelor's degree in Data Science, Artificial Intelligence, Computer Science, Mathematics, Engineering, Business Analytics, or a related discipline. Relevant industry certifications in AI, Machine Learning, Data Science, or Cloud Technologies are advantageous. Experience7–10 years of experience in Data Science, Machine Learning, Artificial Intelligence, or related analytical roles. Proven experience delivering machine learning solutions in enterprise environments. Demonstrated experience working across the complete machine learning lifecycle, from data preparation through deployment and monitoring. Technical SkillsMachine Learning & AI Strong understanding of machine learning and deep learning concepts and methodologies. Experience developing models for both structured and unstructured datasets. Hands-on experience with:TensorFlow PyTorch Scikit-learn Knowledge of model evaluation, optimization, and performance tuning techniques. Natural Language Processing (NLP) Hands-on experience building NLP solutions using:Hugging Face spaCy Gensim Experience working with transformer-based architectures and modern NLP frameworks. Programming & Data Engineering Advanced Python programming skills. Knowledge of Java or R is desirable. Strong experience with:SQL Pandas Apache Spark Experience handling large-scale datasets and distributed data processing. Deployment & MLOpsExperience developing and exposing models through REST APIs. Experience with Docker and containerized deployments. Familiarity with ML lifecycle and experiment-tracking tools such as MLflow. Understanding of CI/CD processes and production deployment best practices. Desirable SkillsExperience working with Large Language Models (LLMs) and Generative AI solutions. Exposure to offline and on-premises AI deployment environments. Experience with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform (GCP). Familiarity with Jupyter Notebooks, Git, and collaborative development workflows. Experience with business intelligence and data visualization tools. Industry experience within Financial Services, Banking, FinTech, Education, or related sectors would be advantageous. Data Scientist in Abu Dhabi, United Kingdom

About the CompanyCareem is building the Everything App for the greater Middle East — making it easy to move around, order food and groceries, manage payments, and more. Our purpose is simple: to simplify and improve people’s lives and build an awesome organisation that inspires.Since 2012, Careem has enabled earnings for over 2.5 million Captains, simplified the lives of more than 70 million customers, and built a platform where the region’s best talent and entrepreneurs thrive. We operate in 70+ cities across 10 countries, from Morocco to Pakistan.We’re now entering our next chapter — one powered by AI. We’re looking for AI talent: curious problem-solvers who know how to apply AI to build tools, automate workflows, and create real impact. Whether it’s streamlining operations, enhancing customer experience, or reimagining internal systems — we want people who can make Careem work smarter and move faster.About The TeamThe Personalization team sits within Careem's Data Science organization and owns the AI systems that decide what every user sees, in what order, and why across Food, Quik, and Shops. Our mission is to build the hyper-personalization layer for the Careem app: real-time, cross-vertical recommendation and ranking systems that learn from a user's behavior in one vertical and apply that understanding everywhere else they engage with Careem. As one of the senior technical leads on this team, you'll help define how Careem thinks about personalization at a regional scale working alongside the region's top data science talent, and pushing the state of the art using graph-based retrieval, transformer architectures, and real-time learning.What You'll DoOwn hyper-personalization use cases across Food, Quik, and Shops designing systems that learn a user's intent and preferences in real time and transfer that signal across verticals, so a user's behavior on one product makes every other product smarter.Be a technical lead on Careem's exploration of graph-based retrieval methods for recommendations including evaluating and building knowledge graph pipelines that power candidate generation and ranking at scale.Design and evaluate transformer-based architectures (XFY) for sequential and contextual recommendation moving Careem's ranking and retrieval stack beyond classical ML toward deep, attention-based models.Push toward online/streaming learning systems that adapt to user behavior within a session, not just from batch-trained models refreshed on a daily cadence.Identify where personalization signals, models, or infrastructure can be shared across Food, Quik, and Shops rather than rebuilt per vertical reducing duplicate work and compounding the value of every experiment.Be part of a 0-to-1 AI transformation for the Careem app from a personalization standpoint shaping how generative AI and LLM-based systems augment retrieval and ranking.Build a long-term vision for how Careem rethinks customer acquisition and engagement strategies, grounded in data-driven decision-making.Drive exploratory analysis to understand user behavior across verticals, identifying new levers to move metrics and building behavioral models that inform product enhancements.Shape and influence the ML models and instrumentation that optimize the product experience, surfacing new areas of opportunity and new product directions.Provide product leadership through data-driven recommendations communicating the state of the business, root-causing metric movements, and using experimentation results to influence product and business decisions.Implement scalable machine learning algorithms that run in production on large-scale data.Run exploratory data analysis to better understand user and business phenomena, and to discover untapped areas of growth and optimization.Answer complex analytical questions from large datasets to help shape Careem's products and services.Define and track key metrics for specific personalization initiatives.Design and run randomized controlled experiments (A/B tests), analyze results, and communicate findings to cross-functional teams.Continually challenge the status quo investigating new data processing technologies, retrieval architectures, and learning paradigms, and ensuring the team operates at industry-leading standards.Build and deploy retrieval-augmented generation (RAG) systems and other applications of large language models within the personalization stack.What You'll Need7-10 years of experience in data mining, predictive modeling, time series analysis, machine learning, and Big Data methodologies, including transformation and cleaning of structured and unstructured data.Advanced degree in a quantitative discipline such as Physics, Statistics, Mathematics, Engineering, or Computer Science.Solid experience with deep learning techniques including attention mechanisms, retrieval models, and transformer-based architectures (XFY or similar) applied to ranking or recommendation problems. Working with or evaluating knowledge graphs, graph neural networks, or graph-based retrieval systems is a strong plus. Careem is actively building toward graph-based retrieval for recommendations.2-4 years of industry experience in personalization, recommendation, or search is a MUST. Preferably gained in a product-driven company operating at scale.Strong problem-solving and coding skills.Solid knowledge of A/B testing methodology, classical ML, and deep learning.Solid understanding of recommendations, ranking, and retrieval systems end-to-end.Familiarity with or interest in online/streaming learning systems models that adapt within a session rather than relying solely on batch retraining, is a strong plus.Proficiency and demonstrated experience in Python, SQL, Spark, and Hive.Demonstrated experience with database technologies (e.g. Hadoop, BigQuery, Amazon EMR, Hive, Oracle, SAP, DB2, Teradata, MS SQL Server, MySQL).Demonstrated experience with business intelligence and visualization tools (Tableau, MicroStrategy, ChartIO, Qlik); geospatial data processing skills are a plus.What We'll Provide YouWe offer colleagues the opportunity to drive impact in the region while they learn and grow. As a full time Careem colleague, you will be able to:Work and learn from great minds by joining a community of inspiring colleagues.Put your passion to work in a purposeful organization dedicated to creating impact in a region with a lot of untapped potential.Explore new opportunities to learn and grow every day.Work 4 days a week in office & 1 day from home, and remotely from any country in the world for 30 days a year with unlimited vacation days per year.Access to healthcare benefits and fitness reimbursements for health activities including gym, health club, and training classes.

Job DescriptionOrganization Unit PurposeEmirates NBD is one of the largest banks in the Middle East, with over USD 6Bn in revenues and 14000 employees globally. As part of its strategic mandate, the bank has recently setup its Advanced Analytics Centre of Excellence for driving ENBD’s ambitious advanced analytics agenda across ENBD.Core objectives of the center include: Deliver 500-800 Mn AED business impact through a series of use cases/ AA projects executed in collaboration with the BU’s Instill a data driven culture across ENBD by building organization wide capabilities in advanced analyticsThe CoE will act as an enabler to the BUs to deploy advanced analytics models to drive smarter decisions across different domains. It will act as a guiding light to BUs to ideate on what’s possible with advanced analytics, help them prioritize these ideas, and pilot and productionalize them through cross-functional AA squads.It will house highly skilled data scientists, data engineers and other digital talent that will be an integral part of cross-functional squads that execute AA projects.The AA agenda is a high visibility and high priority initiative for ENBD. AA CoE will be governed at ExCo level, including impact tracking, a ringfenced budget & performance managementJob Purpose The data scientist scopes, designs, and implements machine-learning models to support use case execution with a view of achieving overall objectives and targets The role is highly collaborative and works with product owners, business translator, data warehousing and data engineering teams in creating big data applications through the utilization of structured and unstructured data, designing optimal data architecture, and experimenting on new machine learning techniques The data scientist is expected to take charge of end to end delivery of machine learning models including regularly testing and maintain analytical models to improve efficiency and keep up with changing business needs The role supports Translators in communicating the design, functioning and output of the analytical models/solutions developed The data scientist additionally plays a mentorship role to others in the team, guiding junior colleagues in execution of their duties, and mentoring them in their professional growthJob ContentDeliver a strong roadmap of AA projects in collaboration with Business UnitsDesigns and implement machine-learning models to support the business’s numerous initiatives and programs with a view of achieving overall objectives and targetsArtefact documentationSuccessful documentation of reusable codeDevelopment and learningAchieve personal technical development and learning goalsCollaboration and knowledge shareConduct knowledge sharing sessions internally within the CoE as well as externally in meetups, conferences etcParticipates in and helps plan cultural initiatives for CoEQualificationsEducationUniversity degreeAdvanced degree (MS/Ph.D) in Computer Applications, Economics, Statistics or Mathematics or related technical discipline with high analytical rigor is desirableExperiences6-8 years of experience in a data science role is essentialKnowledge & SkillsValidated experience of defining analytics road map and leading analytics engagementsShould have experience in machine learning, statistics, optimization or related fieldsExperience with programming is a must; specifically experience in R or PythonExperience of working with large data sets, simulation/ optimization and distributed computing tools (Map/Reduce, Hadoop, Hive, Spark, etc.) is essentialStrong analytical and problem-solving skillsWorked in agile environments with multiple stakeholders from the businessBehavioral CompetenciesPractical and action orientedCompelling communicatorExcellent stakeholder managementFoster and promote entrepreneurial spirit and curiosity amongst team membersTeam playerQuick learnerThere’s never been a better time to join Emirates NBD. We’re one of the region’s most recognizable brands. We’re banking innovation leaders. We’re growing across both the UAE and our global offices. We offer a huge range of professional development opportunities to accelerate your career. It also goes without saying that we provide extremely competitive rewards, benefits and perks too, like our flexible work policy so you can work from home whenever it suits.At ENBD, we encourage interested candidates to review the key responsibilities and qualifications for each role and apply for positions that match their skills and capabilities.

Job DescriptionOrganization Unit PurposeEmirates NBD is one of the largest banks in the Middle East, with over USD 6Bn in revenues and 14000 employees globally. As part of its strategic mandate, the bank has recently setup its Advanced Analytics Centre of Excellence for driving ENBD’s ambitious advanced analytics agenda across ENBD.Core objectives of the center include: Deliver 500-800 Mn AED business impact through a series of use cases/ AA projects executed in collaboration with the BU’s Instill a data driven culture across ENBD by building organization wide capabilities in advanced analyticsThe CoE will act as an enabler to the BUs to deploy advanced analytics models to drive smarter decisions across different domains. It will act as a guiding light to BUs to ideate on what’s possible with advanced analytics, help them prioritize these ideas, and pilot and productionalize them through cross-functional AA squads.It will house highly skilled data scientists, data engineers and other digital talent that will be an integral part of cross-functional squads that execute AA projects.The AA agenda is a high visibility and high priority initiative for ENBD. AA CoE will be governed at ExCo level, including impact tracking, a ringfenced budget & performance managementJob PurposeThe data scientist scopes, designs, and implements machine-learning models to support use case execution with a view of achieving overall objectives and targetsThe role is highly collaborative and works with product owners, business translator, data warehousing and data engineering teams in creating big data applications through the utilization of structured and unstructured data, designing optimal data architecture, and experimenting on new machine learning techniquesThe senior data scientist is expected to take charge of end to end delivery of machine learning models including regularly testing and maintain analytical models to improve efficiency and keep up with changing business needsThe role supports Translators in communicating the design, functioning and output of the analytical models/solutions developedThe senior data scientist additionally plays a mentorship role to others in the team, guiding junior colleagues in execution of their duties, and mentoring them in their professional growthJob ContentDeliver a strong roadmap of AA projects in collaboration with Business UnitsDesigns and implement machine-learning models to support the business’s numerous initiatives and programs with a view of achieving overall objectives and targetsArtefact documentationSuccessful documentation of reusable codeDevelopment and learningAchieve personal technical development and learning goalsCollaboration and knowledge shareConduct knowledge sharing sessions internally within the CoE as well as externally in meetups, conferences etcParticipates in and helps plan cultural initiatives for CoEQualificationsEducationUniversity degreeAdvanced degree (MS/Ph.D) in Computer Applications, Economics, Statistics or Mathematics or related technical discipline with high analytical rigor is desirableExperiences6-8 years of experience in a data science role is essentialKnowledge & SkillsValidated experience of defining analytics road map and leading analytics engagementsShould have experience in machine learning, statistics, optimization or related fieldsExperience with programming is a must; specifically experience in R or PythonExperience of working with large data sets, simulation/ optimization and distributed computing tools (Map/Reduce, Hadoop, Hive, Spark, etc.) is essentialStrong analytical and problem-solving skillsWorked in agile environments with multiple stakeholders from the businessBehavioral CompetenciesPractical and action orientedCompelling communicatorExcellent stakeholder managementFoster and promote entrepreneurial spirit and curiosity amongst team membersTeam playerQuick learnerThere’s never been a better time to join Emirates NBD. We’re one of the region’s most recognizable brands. We’re banking innovation leaders. We’re growing across both the UAE and our global offices. We offer a huge range of professional development opportunities to accelerate your career. It also goes without saying that we provide extremely competitive rewards, benefits and perks too, like our flexible work policy so you can work from home whenever it suits.At ENBD, we encourage interested candidates to review the key responsibilities and qualifications for each role and apply for positions that match their skills and capabilities.

Job OverviewServe as a dedicated Data Scientist within the specialized Miral Destination AI team, working exclusively on initiatives that advance Miral Destination. Analyze Miral Destination data, develop and fine-tune AI and machine learning models, and generate actionable insights that directly support Miral Destination’s business objectives. Partner closely with the ML Ops Engineer to move models from experimentation into production, and report to the Senior Manager AI to ensure all work aligns with the destination’s AI roadmap and priorities.Job ScopeAnalyze Miral Destination datasets to uncover patterns, trends, and opportunities that inform destination decisionsDevelop, train, and fine-tune AI and machine learning models tailored to Miral Destination use casesBuild RAG pipelines, vector databases, and LLM-based solutions on Databricks for Miral Destination applicationsGenerate clear, actionable insights and visualizations that directly support Miral Destination business objectivesCollaborate with the ML Ops Engineer to operationalize models, ensuring they are production-ready and monitoredTranslate requirements from Miral Destination stakeholders, via the Senior Manager AI, into data science deliverablesAlign all work with the enterprise AI architecture, governance, security, and data management standards set by the AI & Data organizationUphold data quality, model performance, governance, and responsible-AI standards across all Miral Destination workCollaborate with AI, Data Engineering, BI, and Enterprise Data teams across DTD to leverage shared capabilities, reusable assets, common platforms, and best practices.Job Essentials3–5 years in Data Science, Machine Learning, or Advanced AnalyticsDemonstrated experience taking models from analysis through to deploymentStrong data analysis, statistical modeling, and machine learning skillsHands-on experience with Python and Databricks (incl. MLflow)AI model development, fine-tuning, and evaluationUnderstanding of RAG, LLMs, and vector databasesData storytelling – turning analysis into clear, actionable insightsCollaboration with ML Ops to deploy and monitor modelsEducationBachelor’s or Master’s in Data Science, AI, Statistics, Computer Science, or related field

Whiteshield combines AI with economic expertise to solve real policy challenges. In the AI Economics unit, you'll turn data into decisions that shape how governments and businesses set policy, manage resources, and plan for growth.You'll combine AI, machine learning, geospatial analysis, and economic modelling to extract actionable intelligence from sources like satellite imagery, trade data, and macroeconomic indicators. You'll design and automate data pipelines, power interactive dashboards and rankings, and build strategy tools that deliver measurable impact.RequirementsWe are looking for strong data scientists and technically literate consultants. We ask people to understand a problem, ideate a solution that addresses the core need, build it technically, then communicate it with clarity and confidence. If you are capable of some of that and eager to develop the rest, we encourage you to apply.Intellectual curiosity, problem-solving drive, and a focus on real-world impact are the prioritiesBenefitsReal influence and access - Shape national policy, competitiveness strategies, and sustainable growth plans while working directly with senior government officials, global institutions, and major corporations. Room to innovate - If you see a better way, try it. We encourage new methods and actively pilot new technologies. Cutting-edge tools - Work with AI, LLMs, predictive modelling, geospatial analytics, and simulation platforms. Structured growth - Learn from senior experts through hands-on mentoring and project-based skill transfer.

Since launching in Kuwait in 2004, talabat, the leading on-demand food and Q-commerce app for everyday deliveries, has been offering convenience and reliability to its customers. talabat’s local roots run deep, offering a real understanding of the needs of the communities we serve in eight countries across the region.We harness innovative technology and knowledge to simplify everyday life for our customers, optimize operations for our restaurants and local shops, and provide our riders with reliable earning opportunities daily.Here at talabat, we are building a high performance culture through engaged workforce and growing talent density. We're all about keeping it real and making a difference. Our 6,000+ strong talabaty are on an awesome mission to spread positive vibes. We are proud to be a multi great place to work award winner.Job DescriptionAs the leading delivery company in the region, we have a great responsibility and opportunity to impact the lives of millions of customers, restaurant partners, and riders. To realize our potential, we need to advance our platform to become much more intelligent in how it understands and serves our users.As a Sr. Data Scientist (AI & ML) on the global AI hub, your mission will be to design, build, and ship the machine learning and generative AI systems that power decisions across product and business. You will own a particular domain end to end, working closely with product and business managers as part of a talented team of data scientists and machine learning engineers. You will own the full ML lifecycle, from problem framing, data modeling, and feature engineering through model training, deployment, serving, and monitoring in production. Many of our initiatives will focus on leveraging Generative AI and LLMs for tasks such as data enrichment, smart content understanding, and automated decision-making to enhance user experiences and business operations at scale.ResponsibilitiesFraming ambiguous business problems as well-defined machine learning and data science problems, with clear, objective success criteria.Providing high-quality, impactful insights and data-driven recommendations through rigorous analysis and automated reporting to drive strategic organizational choices.Designing, building, and shipping end-to-end machine learning and generative AI systems in production — spanning data pipelines, feature engineering, model training, serving, and monitoring.Taking on engineering-heavy work end to end: architecting robust ML-based systems, writing clean and scalable production code, and training, deploying, and maintaining reliable ML models that solve real business problems at scale.Training, evaluating, and iterating on models — selecting the simplest, most appropriate algorithms and architectures to deliver measurable business value.Leveraging LLMs and generative AI for data enrichment, smart content understanding, and automated decision-making within production systems.Building and maintaining the data models, features, and pipelines that power model training and allow us to measure performance and its drivers for your area of focus.Designing, planning, and analyzing experiments (A/B and multivariate tests) to rigorously measure model and product impact.Developing deep familiarity with source data and its generating systems through documentation, collaboration with engineering teams, and systematic data profiling.Partnering with product and business teams to identify high-impact opportunities and translate them into ML solutions and actionable, data-driven recommendations.Mentoring other data scientists in their growth journeys.Elevating engineering and ML best practices — improving our ways of working, tooling, MLOps, and internal training programs.QualificationsTechnical ExperienceDeep expertise in machine learning, generative AI, deep learning, recommendation systems, NLP, pattern recognition, data mining.Deep hands-on knowledge of ML and GenAI frameworks (e.g. Scikit-learn, XGBoost, LightGBM, CatBoost, SVMs, Keras, TensorFlow, PyTorch, Transformers, LLM fine-tuning).Strong software engineering fundamentals: excellent coding skills, a solid grasp of data structures and algorithms, and proven ability in both general system design and ML system design.Proven experience building, deploying, serving, and monitoring ML models in production, with a strong grasp of MLOps practices.Strong data and ML engineering skills, including building and orchestrating data and training pipelines (e.g. via Airflow) and robust feature engineering.Excellent SQL and competence with reproducible analysis and modeling in Python.Solid statistical foundations, including experiment design and analysis (A/B and multivariate) and inferential, causal, and predictive methods.Familiarity with data modeling and dimensional design.Strong command over the entire ML lifecycle, from problem formulation and data auditing through modeling, deployment, interpretation, and presentation.Familiarity with product data (impressions, events, etc.) and product health measurement (conversion, engagement, retention, etc.).Experience with LLMs and NLP-based solutions for data enrichment and smart automation is a plus.Familiarity with BigQuery and the Google Cloud Platform is a plus.QualificationsBachelor's degree in engineering, computer science, technology, or similar fields. A postgraduate degree is a plus but not required.5+ years of experience across data science, machine learning engineering, and generative AI, including shipping ML models to production.Experience building ML systems in an online consumer product setting is a plus.A good problem solver with a 'figure it out' growth mindset.An excellent collaborator.An excellent communicator.A strong sense of ownership and accountability.A 'keep it simple' approach to #makeithappen.

Hyderabad, India / Dubai, UAEFull-timeApply to JobAbout The RoleWe are looking for a highly motivated Machine Learning Engineer / Data Scientist to join our dynamic team. In this mid-level role, you will leverage your expertise in data science and machine learning to design, build, and deploy innovative models and solutions that address real-world challenges.You will collaborate with cross-functional teams, including data engineers, product managers, and software developers, to turn complex datasets into actionable insights and build machine learning solutions that drive business value. This is a fantastic opportunity to work on impactful projects, expand your skill set, and contribute to cutting-edge advancements in AI and data science.Key ResponsibilitiesModel Development: Design, train, and evaluate machine learning models to solve business problems such as classification, regression, clustering, and recommendation.Data Preparation: Work with large and complex datasets, performing data cleaning, preprocessing, and feature engineering to optimize model performance.Model Deployment: Deploy machine learning models into production environments, ensuring scalability and robustness.Algorithm Selection: Research and implement state-of-the-art algorithms and methodologies to enhance model accuracy and efficiency.Collaboration: Collaborate with data engineering teams to build data pipelines and ensure efficient data flow for machine learning workflows.Visualization and Reporting: Create clear and actionable visualizations and reports to communicate findings and results to stakeholders.Monitoring and Maintenance: Monitor model performance in production, address drift or bias issues, and optimize models as needed.Tool Development: Build tools and frameworks to enable rapid experimentation and iteration of machine learning models.Documentation: Maintain comprehensive documentation for models, experiments, and processes.QualificationsEducation:Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, Statistics, or a related field (or equivalent experience).Technical SkillsStrong programming skills in Python, R, or similar languages.Experience with machine learning libraries and frameworks such as TensorFlow, PyTorch, Scikit-learn, or Keras.Proficiency in data manipulation and analysis using tools like Pandas, NumPy, and SQL.Experience with big data technologies such as Spark, Hadoop, or similar.Knowledge of cloud platforms and services (e.g., AWS SageMaker, Google AI Platform, Azure ML).Familiarity with MLOps practices and tools for CI/CD in machine learning workflows.Understanding of data visualization tools like Matplotlib, Seaborn, or Tableau.Strong grasp of statistical methods, probability, and optimization techniques.Experience3–5 years of experience in machine learning, data science, or a related field.Proven experience building and deploying machine learning models in production.Experience with natural language processing (NLP), computer vision, or time-series analysis is a plus.Soft SkillsStrong problem-solving and analytical thinking abilities.Excellent communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.Ability to work independently and collaboratively within a team.Curiosity and eagerness to stay updated on the latest advancements in machine learning and AI.About The CompanyDatamaze is a dynamic company specializing in AI, Data, and Analytics consulting services. We are dedicated to transforming businesses by leveraging the power of data and artificial intelligence. With a team of industry experts in technology, business strategy, and data science, Datamaze offers a range of services including data strategy development, AI model creation, advanced analytics, and data visualization. Our approach is client-centric, focusing on creating customized solutions that address unique business challenges and objectives. The team, comprising data scientists, AI experts, strategists, and engineers, is committed to continuous learning and innovation.Datamaze positions itself as a trusted partner for businesses looking to navigate the complex data and AI landscape, aiming to optimize operations, inform decision-making, and maintain a competitive edge.Apply Now

About RevolutPeople deserve more from their money. More visibility, more control, and more freedom. Since 2015, Revolut has been on a mission to deliver just that. Our powerhouse of products — including spending, saving, investing, exchanging, travelling, and more — help our 80+ million customers get more from their money every day.As we continue our lightning-fast growth,‌ 2 things are essential to our success: our people and our culture. In recognition of our outstanding employee experience, we've been certified as a Great Place to Work™. So far, we have 13,000+ people working around the world, from our offices and remotely, to help us achieve our mission. And we're looking for more brilliant people. People who love building great products, redefining success, and turning the complexity of a chaotic world into the simplicity of a beautiful solution.About The RoleOur Data Science team solves complex problems with smart, practical solutions and improves how customers experience Revolut. Our Deep Learning Engineers are at the forefront of GenAI and LLM integration, building transformative products that range from user-facing tools to advanced process optimisation.We're looking for a Deep Learning Engineer to work with the most advanced LLMs available, developing solutions that have a tangible impact on millions of customers worldwide.You'll collaborate cross-functionally with Product Owners, Software Engineers, Data Analysts, and Operations Managers to deliver automated, scalable solutions that elevate and revolutionise customer interaction.Up to break barriers and shape what's next for the future of Revolut’s AI-driven capabilities? Let's get in touch.What You’ll Be DoingBuilding AI-driven features from scratch, like personal assistants, chatbots, copilots, and moreDeveloping user-focused and backend features using deep learningDelivering impactful, scalable, data-driven AI solutionsCollaborating with Product, Engineering, and Data teams to solve deep learning challengesIntegrating cutting-edge AI technologies to drive innovation at RevolutWhat You'll NeedExperience in deep learning within natural language processing area and large language modelsA bachelor's degree in a STEM major (mathematics, computer science, engineering)Excellent knowledge of data science (Python, SQL) and production toolsA deep understanding of probability and statistics fundamentalsBig-picture thinking to correctly diagnose problems and productionise researchExcellent communication and collaboration skills to partner with Product Owners and business headsNice to haveA master's or PhD in a quantitative disciplineSolid experience with additional programming languages, such as Java, Scala, C++Experience at a large tech company worth >$15BSchool/university Olympic medal competitions in physics, maths, economics, or programmingBuilding a global financial super app isn’t enough. Our Revoluters are a priority, and that’s why in 2021 we launched our inaugural D&I Framework, designed to help us thrive and grow everyday. We're not just doing this because it's the right thing to do. We’re doing it because we know that seeking out diverse talent and creating an inclusive workplace is the way to create exceptional, innovative products and services for our customers. That’s why we encourage applications from people with diverse backgrounds and experiences to join this multicultural, hard-working team.Important notice for candidates:Job scams are on the rise. Please keep these guidelines in mind when applying for any open roles. Only apply through official Revolut channels. We don’t use any third-party services or platforms for our recruitment. Always double-check the emails you receive. Make sure all communications are being done through official Revolut emails, with an @revolut.com domain.We won't ask for payment or personal financial information during the hiring process. If anyone does ask you for this, it’s a scam. Report it immediately.By submitting this application, I confirm that all the information given by me in this application for employment and any additional documents attached hereto are true to the best of my knowledge and that I have not wilfully suppressed any material fact. I confirm I have disclosed if applicable any previous employment with Revolut. I accept that if any of the information given by me in this application is in any way false or incorrect, my application may be rejected, any offer of employment may be withdrawn or my employment with Revolut may be terminated summarily or I may be dismissed. By submitting this application, I agree that my personal data will be processed in accordance with Revolut's Candidate Privacy Notice

Murphy deploys AI agents for banks starting with the most straightforward business case: voice AI for debt collection. We’ve built a fully-fledged platform designed to optimize recovery rates while maintaining respectful and personalized communication.Our AI-powered agents adapt instantly, engaging with debtors across channels like voice, messengers, email and SMS to maximize results while preserving trust.We’re live with Santander, Revolut, BBVA and dozens others. We’ve proven the product in large-scale pilots with top-tier banks. And now we need to move fast to secure leadership in this $300Bn+ industry.About The RoleWe're looking for a Data Scientist to build the Murphy’s brain — the ML that decides who we contact, when, how and how often, to recover the most debt inside hard legal limits. Recovery is a data problem before it's a voice problem, and you own the data problem. This is one of the highest-leverage seats in the company: your models set the strategy every AI agent executes, on every call.Murphy builds AI voice agents that collect overdue debt for banks: more recovered, faster, cheaper, fully compliant, and respectful to the debtor. We're live with Santander, Revolut, BBVA and dozens others. We've proven the product in large-scale pilots with top-tier banks — now we're making every contact smarter, and we need a scientist to own the models that decide how we collect.What you'll doOwn the contact strategy. Who Murphy contacts, when, how often, on which channel — voice, WhatsApp, etc. — to maximise recovery inside hard legal guardrails.Model the debtor. Propensity and uplift models — who picks up, who pays, who responds to which voice, script or offer — turned into live segments the agents act on.Optimise with experiments. A/B tests and bandits over timing, cadence, voice and negotiation policy; find what lifts recovery, and ship it fast.Own business-level outcomes. Collaborate with product, engineering and commercial teams to define solutions and improve the key metrics.Who you areYou hold a degree in mathematics, statistics, machine learning, or computer science.Rigorous with probability and statistics. Experimental design, causal inference. You reach for the right tool, and you know when a result is real.AI-native, ruthless on quality. You reach for AI to automate and streamline by default — and you know exactly where it gets sloppy. You never let that through, and you control quality rigorously.You don’t need a collections or fintech background. We’d rather teach the domain to a killer than hire a lifer who can’t ship — you’ll pick up the domain, the data and the regulation cold, fast.Requirements2+ years shipping ML that ran in production and moved business metrics. Models that pick the best next action and measure whether it actually changed the outcome; ranking, pricing or contact-strategy models. Models that ship as code, not notebooks.Expert Python. Comfortable in a real production codebase — you take a model from idea to production and monitor it. We use Python for ML/AI services and TypeScript around it, on AWS and PostgreSQL.Statistics you can defend. Experimental design, causal inference, measuring the real effect of an action — not just correlations. You know when a result is real, when it’s noise, and how to test it.Experimentation. You’ve designed, run and interpreted A/B tests or bandits on live traffic, and shipped the winner.You turn models into decisions. You can explain to stakeholders why the model says to call this client at 6pm — and defend it.🤝 What we offer💰 Salary at the top of the benchmark.📈 Fair equity with enormous upside potential.🧩 Real Ownership - Murphy's strategy is yours to drive.🙌 Small team, high autonomy, and outsized impact.🗣️ The founding team's direct attention, and a category with a $300B+ industry behind it.🛜 Hybrid & Flexible: Our default setup is hybrid – 3 days a week at our office and 2 days of remote work.📚 Our Process1️⃣ First Interview – Getting to Know YouA conversation with a future teammate who’s excited to find a new colleague. We’ll talk about your story, what drives you, and what you’re looking for next—no trick questions, just a genuine exchange.2️⃣ Second Interview – Deep DiveYou’ll meet the Hiring Manager and potentially another team member. This is a more technical discussion where we explore your skills in detail, walk through real scenarios, and answer any questions you might have about the role.3️⃣ Tech Assessment or Business CaseA practical exercise to see how you approach challenges similar to those you’d tackle at Murphy. You’ll have time to reflect and showcase your thinking—no rush, no surprises.4️⃣ Call with the FoundersOur founders meet every team member and it’s a great opportunity for you to learn about the company and its direction.👉 To learn more about how we hire and what to expect at every step, feel free to explore our Hiring Guide!👉 We are committed to building a diverse, inclusive, and equitable workplace where everyone, regardless of background, identity, or experience, feels valued and empowered to thrive. We believe that different perspectives drive innovation and success, and we actively foster an environment where all voices are heard and respected.