2027 Data Scientist Graduate Programme - Insurance Consulting - London/Reigate

With 49,000 colleagues in over 140 countries, we help organisations make forward-thinking choices about their people, their investments and the risks they face. Joining our Insurance Consulting and Technology (ICT) team, you'll be part of our impact right from the start. Our ICT team uniquely combines deep insurance consulting expertise with innovative technology. We work across all major actuarial pillars within Property and Casualty ("P&C") - reserving, capital, and pricing - as well as broader areas such as data science, process transformation, risk and regulatory, climate, exposure management, underwriting and claims analytics, involving both client projects and research and product development.

At WTW, we believe difference makes us stronger. We want our workforce to reflect the different and varied markets we operate in and to build a culture of inclusivity that makes colleagues feel welcome, valued and empowered to bring their whole selves to work every day. We are an equal opportunity employer committed to fostering an inclusive work environment throughout our organisation. We embrace all types of diversity.

Responsibilities:

• Working with and learning from some of the market's top thought leaders, you'll become part of multiple, varied client projects from day one. These will focus on data-hungry areas such as pricing, claims and operational processes.
• Use data science techniques to analyse data and produce insightful results.
• Present your analysis both internally and to clients.
• Collaborate with colleagues in the UK and globally.
• Build your core technical skills through hands-on experience and team support.
• Deliver data science projects that involve the development, assessment and deployment of machine learning models and writing of high-quality code.
• Learn and apply open-source tools and technology, alongside WTW's own proprietary insurance technology solutions, used widely by the insurance market.
• Grow through structured early-career training and ongoing learning from subject matter experts in the team.

Qualifications Requirements:

• Highly numerate and analytical, with an interest in drawing value from data and applying your skills to complex and challenging commercial problems.
• Experienced in a coding or data-processing language such as Python, R, SQL or similar.
• Genuinely interested in what’s happening in the world of data science, such as recent advancements in Machine Learning and Generative AI.
• A clear and confident communicator who can make complicated ideas understandable and engaging for a range of audiences.
• Personable, approachable and motivated to work as part of a supportive team.
• Someone with an interest in and aptitude for learning and developing new ideas.
• Comfortable using AI tools to support work, with an understanding of their strengths and limitations. Exposure to concepts such as Retrieval-Augmented Generation ("RAG") and Agentic AI would be beneficial but is not required.
• At least a 2:1 degree in a numerate discipline, A-level Mathematics at grade A or above, and at least 136 UCAS points across three A levels, or equivalent qualifications. For example, AAB or A*AC would meet the overall points requirement.

Closing Date: 19th October 2026

Workstyles: We believe in the value of in-person learning at this early stage of your career, so you'll be expected to come into the office at least four days a week. You'll be based in our London or Reigate office. Your office location will be agreed with you during the onboarding process. Regardless of your allocated office, you may be expected to travel to other WTW locations for team or client meetings.

We're committed to equal employment opportunity and provide application, interview and workplace adjustments and accommodations to all applicants. If you foresee any barriers, from the application process through to joining WTW, please email candidatehelpdesk@wtwco.com. We are committed to maintaining a fair and equitable process for all candidates.