Identify and prioritise high-value analytical opportunities that drive measurable improvements in pricing performance (including loss ratio, growth, and retention)
Lead discussions with business stakeholders to identify how data science can improve decision-making and outcomes
Develop and deploy predictive models to support pricing and underwriting decisions, including risk cost modelling, demand modelling, and price optimisation
Translate model outputs into clear pricing recommendations and insights that influence underwriting and portfolio decisions
Monitor and evaluate model performance, defining refresh and recalibration requirements
Design and prototype analytical tools and applications for business users (e.g. underwriters, claims handlers)
Work closely with data engineering and data platform teams to source, structure, and prepare data for modelling
Lead the development of new analytical propositions to enhance core insurance functions
Lead analytical projects and coordinate delivery across teams
Mentor junior team members and support their development

Insurance experience is preferred but not essential
Strong knowledge of statistical / data mining methods and application in a business environment
Good understanding of Data Science domain, statistical and analytical model development and implementations, proficient in GLMs, machine learning techniques and related disciplines
Good understanding of data modelling techniques, tools/language – (preferably Python)
Good knowledge of visualization tools like PowerBI, Tableau etc.
Experience of insurance pricing tools (such as Emblem and Radar) is preferred but not essential

Pricing Data Scientist

Key skills