Lead Data Scientist/AI Engineer

Role OverviewAs a Lead Data Scientist/AI Engineer, you will independently work on specific data projects and be responsible for implementing analytical solutions. You will design, build, deploy, and support end-to-end Data & AI solutions. You will translate complex business challenges into scalable, production-ready analytics and machine learning systems, collaborating closely with product, data engineering, and architecture stakeholders to deliver measurable impact.About ContangoContango is the strategic partner for transformative growth and sustained success for the ADQ portfolio. Our team excels in providing comprehensive growth solutions that combine global best practices with local market expertise. We focus on long-term value creation, empowering our clients to achieve the full scale of their aspirations. As a trusted advisor to ADQ's portfolio companies, Contango helps CEOs drive strategic growth initiatives, navigate disruptive forces, and maximize long-term value creation.Key ResponsibilitiesUse Case Framing & Solution DesignTranslate client business problems into end-to-end system architectures that combine Data, ML, and software components.Lead the design of scalable, modular AI solutions, defining services, interfaces, and data flows.Make explicit trade-offs across performance, cost, latency, and maintainability.Define success metrics, SLAs, and non-functional requirements (reliability, security, scalability).Data Engineering & Feature SystemsDesign and implement robust data pipelines (batch and streaming) with strong guarantees on quality, lineage, and observability.Build and manage feature pipelines and feature stores, ensuring consistency between training and inference.Collaborate with platform teams to define data models, schemas, and storage strategies.Enforce standards for data validation, testing, and monitoring within production systems.Applied ML & Production-Grade DevelopmentDevelop ML solutions using production-quality code (Python/JS), following software engineering best practices.Structure codebases into maintainable, testable modules, with clear separation of concerns.Implement unit, integration, and end-to-end tests for data and ML components.Package models and logic into deployable services (APIs, microservices, batch jobs) using modern frameworks.Balance model sophistication with system performance, latency, and operational constraints.MLOps, DevOps & Platform IntegrationBuild and maintain CI/CD pipelines for ML systems, including automated testing, validation, and deployment.Containerize and deploy services using Docker, Kubernetes, and cloud-native tooling.Implement model versioning, experiment tracking, and artifact management.Design monitoring and observability systems (logs, metrics, alerts) for both data and model performance.Automate retraining, rollback, and release strategies to ensure system resilience.System Reliability, Scalability & SecurityDesign systems for high availability, fault tolerance, and horizontal scalability.Optimize performance across data pipelines and inference services (latency, throughput, cost).Apply secure coding practices, access controls, and data protection standards.Manage technical debt and ensure long-term maintainability of production systems.Documentation, Standards & Engineering ExcellenceProduce developer-focused documentation (APIs, architecture diagrams, runbooks).Establish and enforce coding standards, review processes, and engineering best practices.Build reusable libraries, SDKs, and internal frameworks to accelerate delivery.Drive continuous improvement in engineering maturity, tooling, and delivery practices across the consultancy.Required Experience And QualificationsPrior experience at management consulting firms and/or Big Tech is an advantage.Client-serving experience is an advantage.8+ years of experience in data science or a related analytical field.8+ years delivering end-to-end analytics/ML solutions from problem framing through deployment and ongoing monitoring in production.Experience applying software engineering methodologies and best practices, including coding standards, code reviews, build processes, testing, and security.Prior experience in developing AI solutions on public cloud services is an advantage.Bachelor’s degree (Master’s preferred) in a quantitative field (Computer Science, Data Science, Statistics, Mathematics, Engineering).Technical Expertise:Coding & data querying: Python (pandas/NumPy) and SQL; writing clean, testable code with Git.Statistics & experimentation: Probability/statistics, hypothesis testing, regression, and A/B testing/experimental design.ML modelling: Feature engineering, model selection, cross-validation, metrics, and hyperparameter tuning (supervised/unsupervised).Data prep & analysis: ETL/EDA, data cleaning, handling missing/outliers, and building insight narratives with visuals.