Data Scientist

We are looking for a Data Scientist to join our clients pricing and analytics team. This role is ideal for someone early in their career who combines strong statistical thinking with an interest in insurance economics, especially in areas such as pricing, reserving, and telematics-driven analysis. You will partner closely with senior pricing and actuarial leaders, working across the full analytical lifecycle from raw data exploration to business-facing recommendations. The position offers broad exposure to how insurance performance is measured, explained, and improved through disciplined modeling and clear communication.

Responsibilities:

• Perform end-to-end analytical work that supports insurance rate reviews, including data preparation, trend evaluation, loss development analysis, and documentation used for regulatory support.
• Develop, test, and refine pricing and telematics models using statistical and machine learning techniques, ensuring results are accurate, explainable, and suitable for business use.
• Assist with reserving analysis by working with loss triangles, development patterns, and ultimate loss estimates that align actuarial findings with financial reporting.
• Create and maintain recurring performance dashboards and reporting that track key indicators such as loss ratios, claim frequency, claim severity, and cohort behavior.
• Investigate unusual shifts in portfolio performance, segment results, or customer retention trends, and translate findings into actionable insights for the broader team.
• Write organized, reusable code and data workflows that support independent analysis while making it easy for others to review and build on your work.
• Collaborate with pricing and actuarial stakeholders to gradually take ownership of increasingly complex analyses and modeling assignments as your experience grows.
• Communicate analytical conclusions clearly in written materials and presentations for audiences that need business impact explained without technical detail.
• At least 1 year of experience in actuarial analysis, insurance data science, or a closely related analytical role; strong early-career candidates with relevant internship experience may also be considered.
• Proficiency in Python and SQL, with the ability to extract, clean, analyze, and structure data independently.
• Solid understanding of statistical modeling principles, including the ability to explain how models work rather than simply applying packaged methods.
• Regular use of AI-enabled tools to improve coding, analysis, and learning efficiency, along with sound judgment about their strengths and limitations.
• Strong attention to detail, intellectual curiosity, and persistence in resolving unexpected data patterns or analytical results.
• Clear written communication skills with the ability to explain findings to stakeholders who may not have a technical background.
• Experience with tools or environments such as BigQuery, telematics data, machine learning libraries, or progress toward actuarial exams is a plus.