Data Scientist

We’re looking for a hands-on Senior Data Scientist / ML Engineer to help build and deploy scalable machine learning solutions across financial products.This is a practical, business-focused role for someone who enjoys taking models from experimentation through to production and working closely with engineering, product, and business teams to drive measurable impact.What You’ll Do:Build and deploy machine learning models for real-world business problemsDevelop end-to-end ML pipelines including data preparation, training, validation, deployment, and monitoringWork with large and complex datasets to generate actionable insightsPartner with engineering teams to productionize models and APIsCollaborate with stakeholders across product, risk, operations, and analyticsImprove model performance, reliability, and scalabilityContribute to MLOps, automation, and deployment best practicesSupport AI and advanced analytics initiatives across the businessRequired Skills & Experience:6+ years of experience in Data Science, Machine Learning, or ML EngineeringStrong Python and SQL skillsExperience building and deploying ML models into production environmentsStrong understanding of machine learning fundamentals and model evaluationExperience working with cloud platforms (AWS, Azure, or GCP)Experience with tools/frameworks such as:scikit-learnPyTorch or TensorFlowSpark or DatabricksDocker / CI-CD pipelinesExperience building APIs or production servicesAbility to work with both technical and non-technical stakeholdersPreferred:Banking, fintech, or financial services experienceExperience with customer analytics, fraud, or decisioning modelsFamiliarity with MLOps and model monitoringExposure to NLP, LLMs, or Generative AI use casesExperience with Airflow, MLflow, Kubernetes, or Hadoop ecosystemsWe value people who:Take ownership and solve problems end-to-endCan balance experimentation with production deliveryThink commercially and focus on business outcomesCommunicate clearly and work well across teamsEnjoy building scalable, reliable systemsTech EnvironmentPython • SQL • Databricks • Spark • Docker • APIs • Cloud Platforms • MLflow • CI/CD • Power BI