Lead Data Scientist/AI Engineer

Contango

Key Responsibilities

• Use Case Framing & Solution Design

  • Translate 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 Systems

  • Design 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 Development

  • Develop 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 Integration

  • Build 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 & Security

  • Design 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 Excellence

  • Produce 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.

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.