Lead the building and scaling of next-generation risk models, overseeing everything from creative feature discovery and advanced modeling to robust pipeline deployment and long-term R&D strategy
Establish a reliable Data Science workflow: clear guard-rails, templates, and documentation across the full project lifecycle
Shorten the idea-to-production cycle: from first data pull to live rate change
Foster a culture of collaborative experimentation: pairing Pricing, Data Science, Machine Learning Engineering & Systems Engineering
Line management and mentorship: manage 2 Data Scientists and mentor members of the team and help them in their development
Define coding standards and best practice, map common processes and establish playbooks for them
Help decide on tooling used during the Data Science project lifecycle

Bachelor’s or Master’s degree in Statistics, Mathematics, Actuarial Science, Data Science or related field
At least 5 years proven experience in insurance pricing and applied ML, including leading projects and people, with a particular focus on Data Science operations
Strong Python programming skills, with experience in developing and maintaining analytics packages and tools. Familiarity with data science libraries such as pandas and scikit-learn
Proficient in SQL, particularly with cloud data warehouses like Snowflake
In-depth knowledge of GLMs and other machine learning algorithms
Familiarity with cloud-based data platforms (we’re on AWS) and data visualisation tools (Looker, matplotlib, seaborn)
Experience with version control systems (e.g., Git), CI/CD pipelines, and software development best practices in a data-intensive environment
Excellent communication and project management skills, with a proven ability to work collaboratively across teams and present complex information in a clear, accessible way
Care about documentation, reproducibility, and teaching others “the why” behind decisions

Lead Data Scientist

Key skills