Lead Data Scientist

Plymouth Rock Assurance is on a mission to apply advanced data science to deliver breakthrough insights that propel us to the forefront of personal lines insurance. The Enterprise Data Science team sits at the center of the company, partnering with business leaders to deliver solutions that create durable competitive advantage.

We are seeking a highly motivated and technically skilled lead data scientist to join our collaborative, fast-paced, entrepreneurial team.  We are a high-visibility team focused on transformative analytics that drive profitable growth and improve operational performance across the entire enterprise, including Product, Pricing, Underwriting, Claims, Customer Service, and Marketing.  This is not a “support” analytics role. You will work on high-impact problems, build production-grade solutions, and use modern machine learning and AI to accelerate discovery, improve decision-making, and reshape how we compete. 

Responsibilities:

Depending on level (Data Scientist, Senior, or Lead), you will own projects end-to-end from problem framing through deployment, or lead critical workstreams with broad autonomy:

• Identify and frame high-value problems across functional areas; translate business questions into analytical strategies, experiments, and measurable outcomes.
• Develop, test, and deploy predictive models that drive profitable growth and improve operational performance across the enterprise.
• Apply modern ML and AI techniques to accelerate development cycles, improve model performance, and deliver new capabilities.
• Build production-ready solutions: robust data pipelines, feature engineering, measurement discipline (KPIs, guardrails, and experiment design), model monitoring, and clear, reproducible documentation aligned to best practices.
• Communicate with impact: tell the story with data, present recommendations to technical and non-technical stakeholders, and influence decisions at senior levels.
• Advance team excellence: evaluate new methods and tools, share reusable components, elevate engineering standards, and (at Senior/Lead) mentor others and help shape technical direction.

Qualifications: 

• PhD in a quantitative field (PhD strongly preferred).
• Strong foundation in statistics and applied modeling—you can connect theory to practical, business-relevant solutions.
• Strong hands-on experience with modern modeling tools and methods, including:
• Python (strongly preferred) and/or R for statistical modeling
• SQL for large-scale data transformation and analysis
• GLMs and tree-based methods/GBMs (e.g., H2O, XGBoost, LightGBM); familiarity with clustering, Bayesian methods, regularization, and optimization is a plus
• Experience with AI (e.g., NLP/LLMs, deep learning, computer vision) applied to feature generation, model development, and business process improvement is helpful but not required.
• Ability to deliver results in real-world settings: structured problem-solving, experimental mindset, and pragmatic decision-making.
• Senior candidates must have a proven track record of end-to-end model ownership including shipping models into production, and improving them through monitoring, measurement, and iteration.
• Strong communication skills—able to present and explain methods, assumptions, tradeoffs, and results clearly.
• Experience working with cloud and modern data platforms (especially AWS: S3, EC2, SageMaker; and Snowflake).
• Strong grasp of relational databases and experience working with large, multi-source datasets.
• Comfort working in Git-based, version-controlled environments; strong documentation practices are required.
• Insurance industry experience is helpful but not required.

Why This Role is Unique

• Strategic Impact: See the direct business value of your models on core growth and profitability levers across the enterprise.
• High Visibility: Present directly to the Enterprise Chief Advanced Analytics Officer and other senior executives.
• End-to-End Ownership: Own solutions from data wrangling and feature engineering through model development, deployment, and monitoring in production.
• Innovative, Entrepreneurial Environment: Test new ideas quickly in an agile, responsive culture that embraces a “Do It Now” mindset with rigorous measurement and engineering discipl