Lead Data Scientist/AI Engineer
Skills
About This Role
• 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.
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