Senior Data Scientist - AI

Description

We are seeking a Data Scientist with experience prototyping, building, and scaling AI applications to join WTW’s Data Science team within our Consulting practice. In this role, you will help grow our AI and Data Science advisory capability, working alongside leading insurance market experts to design and deliver cutting edge machine learning and generative AI solutions for the world’s leading general insurers and intermediaries.

As a data scientist, you will operate at the intersection of advanced machine learning, generative AI, and insurance domain expertise. You will help shape, prototype, and scale AI-driven solutions that materially transform how insurers analyse risk, operate, and make decisions.

• Design, prototype, and build applied AI solutions — including agentic workflows — that enhance consulting delivery, unlock new analytical capabilities, and augment expert judgement.
• Develop generative AI applications using techniques such as Retrieval‑Augmented Generation (RAG), prompt engineering, and tool‑augmented agents to convert complex, unstructured insurer data into reliable, structured insights, prototypes, and semi‑automated analytical tools.
• Own AI solutions end‑to‑end, from translating ambiguous problem statements into working prototypes through to production‑ready applications for internal teams and external clients.
• Identify and deliver automation opportunities, such as natural language interfaces over insurer datasets, AI‑assisted exploratory analysis, document understanding, and generation of structured analytical outputs.
• Apply rigorous AI evaluation and safety practices, including:
  • Prompt and few‑shot experimentation
  • Hallucination detection and mitigation
  • Deterministic and rule‑based validation
  • SME‑driven constraints and edge‑case testing
  • Human‑in‑the‑loop feedback mechanisms
• Rapidly prototype under uncertainty, independently converting loosely defined business questions into functioning AI tools with minimal supervision and tight iteration cycles.
• Translate domain expertise into machine‑readable logic, integrating subject‑matter expert input into rules, checks, evaluation criteria, and structured reasoning constraints.
• Collaborate with consulting, product, and technology teams to scale successful prototypes into reusable, robust analytical workflows and AI‑enabled offerings.
• Deliver best‑in‑class data science and AI capability reviews, helping clients assess maturity, identify opportunities, and design practical AI roadmaps.
• Build predictive models, analytical tools, and data products using a wide range of machine learning, statistical, and data science techniques across structured and unstructured data.
• Leverage market and client knowledge to co‑create innovative solutions across WTW, acting as a visible advocate for WTW’s Data Science and AI consulting capabilities.
• Manage substantial work streams on large, complex projects, including day‑to‑day client engagement, delivery ownership, and coordination across practices and regions.
• Develop trusted advisor relationships with senior client stakeholders through clear communication, technical credibility, and consistently high‑quality execution.
• Contribute to intellectual capital and proposition development, supporting business development through proposals, thought leadership, and internal collaboration.
• Lead, mentor, and develop junior colleagues, managing small teams to deliver projects on time, on budget, and to a high technical standard.

• Fluent Python programming skills with an understanding of software engineering practices
• Demonstrable experience designing, developing, and deploying AI applications end to end, from initial prototype through to scalable production use
• Hands on experience with one or more of the following Generative AI techniques:
• Retrieval Augmented Generation (RAG)
• Prompt engineering
• Agent based or agentic AI workflows and orchestration
• AI evaluation frameworks
• Neuro symbolic frameworks
• Experience with major cloud platforms such as Azure or AWS
• Strong understanding of machine learning and statistical modelling principles, with the ability to explain model behaviour, assumptions, and…