Director of Data Science

Role Overview

• Lead Personal Insurance Actuarial Modeling: Serve as the model owner for personal insurance predictive models within actuarial, ensuring alignment with ratemaking and business priorities.
• Partner on strategic vision while managing team capacity and driving timely model deliverables.
• Model Development & Deployment: Lead design, maintenance, and deployment of predictive models for frequency, severity, and loss cost estimates, ensuring production readiness and effective integration into actuarial workflows.
• Cross-Functional Collaboration: Partner with Actuarial, Data Engineering, and other modeling organization teams to connect modeling initiatives with ratemaking strategies and organizational goals, promoting shared problem-solving.
• Innovation: Drive modernization through advanced modeling techniques, machine learning, and AI to enhance efficiency and decision-making.
• Lead experimentation with new data sources, features, and methodologies to uncover insights and advance strategic objectives.
• Operational Excellence: Ensure robust documentation, validation, and governance, along with scalable processes to monitor model performance and maintain reliability.
• Talent Development: Mentor and develop team members, fostering a culture of curiosity, ownership, and continuous learning.
• Support hiring and onboarding, including intern and actuarial student rotations.
• Strategic Influence: Participate in enterprise initiatives to ensure personal insurance predictive models are efficiently integrated into core data and system platforms.

Requirements

• 8+ years of relevant analytical experience recommended
• Master’s or Ph.D. in Statistics, Applied Mathematics, Quantitative Economics, Actuarial Science, Data Science, Computer Science, or a similar analytical field, or a relevant professional designation (e.g. FCAS, FSA, CSPA, ACAS, ASA)
• Expertise in statistical modeling, inference, and building machine learning algorithms in Python and/or R
• Prior Management Experience
• Expertise in the end-to-end modeling lifecycle, from requirements gathering to monitoring and validation
• Experience in SQL and familiarity with cloud-native environments (e.g., Snowflake, Sagemaker)
• Able to communicate effectively with both technical and non-technical audiences
• Able to translate complex technical topics into business solutions and strategies as well as turn business requirements into a technical solution

Tech Stack

• Cloud
• Python
• SQL

Benefits

• short-term or annual bonuses
• long-term incentives
• on-the-spot recognition