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ML Data Scientist
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Lead ML Data Scientist
Global financial services firm | Full time
Build and run production AI at scale. Lead the design, deployment and operation of machine learning, GenAI and agentic systems for complex financial operations, working closely with product, risk, operations and technology.
The Role
- Own delivery end to end, from problem framing to production rollout
- Build ML, GenAI and multi-agent solutions with clear evaluation, safety and monitoring
- Set production patterns: containerisation, CI/CD, automated testing, model and prompt registries, rollback
- Deploy secure, scalable ML and LLM services via APIs, batch, streaming and event-driven patterns
- Create reusable components: feature engineering, evaluation harnesses, agent frameworks, orchestration, memory management
- Mentor engineers and data scientists through code and design reviews
- Translate model outputs into decisions and trade-offs for non-technical stakeholders
- Maintain model cards, runbooks and audit-ready documentation
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Essential
- Master's in a quantitative field, or equivalent experience
- Strong ML fundamentals and applied data analysis
- Proven production ML at scale: monitoring, drift, reliability, incident management
- Strong Python engineering: modular OO design, testing, performance tuning
- MLOps and LLMOps: training and serving patterns, feature stores, orchestration
- Rigorous offline and online evaluation, with guardrails
- Experience in regulated environments: model risk, controls, privacy, security
- Strong stakeholder management
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Desirable
- LLMs, RAG, tool calling, agentic workflows, multi-agent orchestration
- MCP and agent interoperability standards
- PyTorch, TensorFlow, scikit-learn, pandas, SciPy, statsmodels
- AWS (SageMaker, Bedrock) with focus on cost, latency and scaling
- Human-in-the-loop, active learning and labelling strategies
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