Lead Data Scientist/AI Engineer

Role Overview

About Contango

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.

Required Experience and Qualifications

• Prior experience in management consulting companies and/or big tech 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.