Senior Quant AI Analytics

The Senior Quant / Analytics Specialist plays a key role in shaping applying and interpreting quantitative models and analytics that support pricing risk portfolio and strategic decision making across Commodities Trade and Transition (CTT). In this role you will own and build real production analytics that influence trading/pricing/risk decisions shaping standards and approach end to end and be deciding pragmatically where ML / AI adds value over time (not mandated to deploy every new AI technique). As well as this you will be ensuring delivery and ownership sit within WIB CTT aligned to CTT cost centre / roles.

Key Responsibilities include:

• Lead end to end operationalisation of ML GenAI analytics:
  - Model lifecycle: design build validate release monitor retire.
  - Production controls: versioning reproducibility audit trails change management.
• Own GenAI technical implementation quality:
  - Retrieval strategy and RAG architecture; define context contracts per use case.
  - Define and enforce evaluation strategy (metrics coverage red team tests drift).
  - Establish prompt/context release discipline (versioning rollbacks safe defaults).
• Deliver analytics that are reliable scalable governed in production.
• Maintain WIB CTT ownership boundary use cases artefacts and outcomes (models evals dashboards decision integrations) for our line of business
  

Were looking for a highly capable quantitative and AI specialist who brings strong depth across both classical machine learning and modern GenAI engineering. Youll be comfortable taking complex models from concept through to production setting high technical standards and leading sophisticated AI work end to end while also supporting the growth of others along the way. The ideal candidate will have strong experience in delivering production ML systems (not just notebooks) and will provide a demonstrated ownership of model lifecycle and operationalisation. You should well versed in delivering GenAI features with measured evaluation and controlled releases.

Additional skills and experience required includes:

• Strong modelling depth across classical machine learning used in production decisioning including forecasting optimisation anomaly detection and risk models with a practical understanding of realworld tradeoffs.
• Comfortable partnering with traders/risk producing decision relevant analytics
• Deep GenAI engineering experience including:

- Designing and selecting RAG architectures (hybrid retrieval reranking metadata strategies).
- Building robust evaluation frameworks covering coverage failure modes calibration drift and regression.
- Implementing safety patterns such as grounding clear refusal boundaries deterministic fallbacks and strong auditability.

• A disciplined approach to production delivery including release engineering observability incident response and defining measurable SLOs for models.
• The ability to set technical standards and lead complex quantitative and AI initiatives end to end.
• A genuine interest in teaching mentoring and developing junior team members.
• Stack experience should include: Microsoft Azure / Snowpark Container Services Airflow Python Mongo Kafka REST API Microservices React LangChain / LlamaIndex / AutoGen and snowflake