Sr Data Scientist ; Actuarial Science

Would you like to apply actuarial science and statistical modeling to build predictive models that directly influence underwriting, pricing, and risk decisions for insurers at scale?

We are seeking a Senior Data Scientist I with a strong actuarial foundation to join our Insurance Analytics team, focused on the commercial insurance market. In this role, you will design and develop predictive models that are embedded in carrier workflows and used to inform underwriting decisions, segmentation strategies, and downstream pricing models.

Unlike traditional actuarial roles, you will focus on building external‑facing risk models and attributes that insurers integrate into their pricing and underwriting frameworks. This is an ideal opportunity for someone with actuarial training who enjoys applying statistical modeling and analytical thinking to broader insurance problems at scale.

• Develop predictive risk models and attributes used by insurers in underwriting, segmentation, and decision‑making workflows.
• Apply actuarial principles and statistical modeling techniques to assess risk and improve model performance.
• Design and implement models that are integrated into carrier underwriting processes and downstream pricing frameworks.
• Translate complex analytical outputs into clear, defensible insights for business and product stakeholders.
• Partner with Product and Vertical teams to solve insurance‑specific problems related to risk evaluation and segmentation.
• Manage and analyze large, complex datasets, including data storage, processing, and quality assurance.
• Apply best practices for data validation, testing, and model performance monitoring.
• Collaborate with team members to share knowledge, strengthen capabilities, and contribute to a strong analytical culture.
• Maintain a strong understanding of team tools, technologies, and evolving industry trends.
• Communicate progress, insights, and outcomes clearly to stakeholders.
• Support team excellence by upholding high standards of quality, accountability, and execution.

• Minimum undergraduate degree in a relevant field and 4+ years of relevant work experience.
• Or a master’s degree in a relevant field and 2+ years of relevant work experience.
• Or a PhD in a relevant field.
• Strong actuarial foundation, including experience applying actuarial concepts to insurance risk, underwriting, or segmentation problems.
• Progress toward actuarial credentials (ASA or equivalent) strongly preferred.
• Strong expertise in Python; coding skills in R, SQL, ECL are a plus.
• Experience developing or supporting risk segmentation models (e.g., GLMs) in an insurance context and in Department of Insurance filings.
• Experience translating actuarial models into production‑ready analytical solutions.
• Strong foundation in statistical and mathematical modeling, including model assumptions, diagnostics, and interpretability.
• Extensive programming skills in Python and/or R for statistical modeling and data analysis.
• Strong ability as a self‑starter to learn new technologies and to share cross‑functional knowledge across teams (nice to have).

• Able to build or test new processes with senior guidance; domain expert in Data Science, Actuarial Science and/or Statistical Analysis to build advanced models and roll into production.
• Scope and execute analytical approaches for moderately complex problems, seeking input where needed.
• Support, maintain, and enhance existing models (e.g., GLM and tree‑based methods).
• Apply statistical, mathematical, predictive modeling and analytical techniques to work with large, complex datasets from diverse sources.

• Independently prepare, clean, and transform data for analysis and modeling.
• Apply a range of data processing techniques and explore new methods to improve data quality and usability.

• Own and deliver components of projects independently, including planning and execution of key tasks.
• Contribute to larger, more complex projects by executing defined work streams and meeting timelines.

• Experience working with insurance data, risk modeling, or underwriting‑related…