Senior Data Scientist II

3+ years of experience in insurance data analytics, preferably within the Property & Casualty domain, with a strong understanding of underwriting, claims, and pricing workflows.
Hands-on experience developing pricing models using statistical, actuarial, or machine learning techniques to support rate-making, segmentation, and profitability analysis.
Participation in cross-functional initiatives, demonstrating strong collaborative skills and business acumen.
Strong communication skills, with a track record of presenting technical findings to technical and non-technical stakeholders.
Proficiency in Python.
Bachelor’s degree in a quantitative field such as Statistics, Mathematics, Actuarial Science, Computer Science, Data Science, or a related discipline.

Design and develop advanced insurance pricing models using statistical and machine learning techniques to support strategic pricing decisions and revenue optimization.
Perform ongoing model validation to ensure robustness, accuracy, and compliance with internal and regulatory standards.
Monitor model performance over time, identifying drift, degradation, or anomalies, and implement corrective actions as needed.
Build and deploy analytics solutions that address complex business challenges, leveraging large-scale datasets to generate actionable insights.
Collaborate closely with cross-functional teams including actuarial, underwriting, and product to align modeling efforts with business goals.
Communicate findings and recommendations clearly to technical and non-technical stakeholders through presentations, dashboards, and reports.
Document model development processes, assumptions, and validation results to ensure transparency and reproducibility.
Take initiative to support broader team goals and perform additional tasks as needed to drive business success.
Demonstrate strong teamwork and collaboration, actively contributing to a positive and inclusive team culture.
Represent the organization professionally in all interactions with internal and external stakeholders.
Stay current with industry trends in insurance pricing, data analytics, and machine learning to continuously improve modeling approaches.

Solid understanding of actuarial ratemaking principles, including exposure rating, loss cost modeling, and rate adequacy analysis.
Proficiency in PySpark for distributed data processing and pipeline development.
Experience with SQL for querying and manipulating large-scale structured datasets.
Experience with cloud-based analytics environments.
Experience working with actuarial teams or in a blended actuarial/data science environment.
Progress toward the Associate of the Casualty Actuarial Society (ACAS) designation.