Design and deliver GenAI solutions including LLM/RAG (retrieval strategy, embeddings, vector stores, prompt flows, grounding) for enterprise use cases.
Evaluate and improve solution quality using offline/online metrics (quality, latency, cost) and iterate based on feedback.
Harden solutions for production with observability/monitoring, tracing, guardrails, safety controls, and reliability practices
Build and integrate model endpoints into products and workflows (APIs/services), partnering with engineering through to deployment.
Work across cloud platforms (Azure/AWS/GCP) integrating storage, compute, orchestration, and model/runtime components.
Assess data readiness for modelling/RAG (fitness, quality, access) and define remediation requirements
Collaborate in cross-functional squads (DS/DE/engineering/product) and contribute to reusable assets and ways of working.
Communicate clearly with stakeholders on trade-offs, evaluation results, risks, and adoption actions.
Own end-to-end workstream delivery, lead stakeholder conversations, mentor others. (more senior levels)
Shape solution direction and quality bar, coach teams, contribute to sales pursuits/bids and accelerators (most senior levels)

Essential Skills:
Strong Python/R (pandas/NumPy; ML libs such as scikit-learn; DL frameworks TensorFlow/PyTorch).
Experience with LLM/RAG toolchains (e.g., LangChain, LlamaIndex, Semantic Kernel) and vector search (e.g., Pinecone, Weaviate, FAISS, Azure AI Search).
Experience with GenAI platforms (e.g., OpenAI API, Anthropic, Gemini, Llama or equivalents).
Exposure to big data/distributed computing and pipeline/feature engineering.
LLM safety & governance (hallucination mitigation, grounded responses, audit trails)
Degree in a quantitative field
Right to work in the UK without sponsorship
Preferred Skills:
Cloud ML experience (AWS/GCP/Azure).
Strong SQL; experience with visualisation tools (Tableau/Power BI or Python viz).
Specialisms: NLP / computer vision / time series.
NoSQL familiarity.
Quant / trading analytics engineering practices
Time-series forecasting (prices, demand, blend outcomes, scheduling effects)
Optimisation / simulation (planning, blending, logistics constraints)
Model risk controls (bias/leakage checks, backtesting discipline, monitoring/drift)
CI/CD, deployment, monitoring; Docker/Kubernetes.
Experiment design and randomised trials.
MSc with PhD a plus
Personal attributes
Analytical, pragmatic problem-solver; outcome-oriented.
Self-directed, able to prioritise and juggle multiple workstreams.
Clear communicator who can simplify complexity.
Collaborative, curious, continuous learner.

Data Scientist – GenAI – Consultant

Key skills