Job description

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. 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 discipline. 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 Salary Range: The pay range for this position is $152,000 to $217,000 annually. Actual compensation will vary based on multiple factors, including employee knowledge and experience, role scope, business needs, geographical location, and internal equity. Perks and Benefits: • 4 weeks accrued paid time off + 9 paid national holidays per year • Free onsite gym at our Boston Location • Tuition Reimbursement • Low cost and excellent coverage health insurance options that start on Day 1 (medical, dental, vision) • Robust health and wellness program and fitness reimbursements • Auto and home insurance discounts • Matching gift opportunities • Annual 401(k) Employer Contribution (up to 7.5% of your base salary) • Various Paid Family leave options including Paid Parental Leave • Resources to promote Professional Development (LinkedIn Learning and licensure assistance) • Convenient location directly across from South Station and Pre-Tax Commuter Benefits About the Company The Plymouth Rock Company and its affiliated group of companies write and manage over $2 billion in personal and commercial auto and homeowner’s insurance throughout the Northeast and mid-Atlantic, where we have built an unparalleled reputation for service. We continuously invest in technology, our employees thrive in our empowering environment, and our customers are among the most loyal in the industry. The Plymouth Rock group of companies employs more than 1,900 people and is headquartered in Boston, Massachusetts. Plymouth Rock Assurance Corporation holds an A.M. Best rating of “A-/Excellent” #LI-SM1

Requirements

Must-haves

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 discipline.
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.
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.
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:
SQL for large-scale data transformation and analysis
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.

Nice-to-haves

PhD in a quantitative field (PhD strongly preferred).
Python (strongly preferred) and/or R for statistical modeling
GLMs and tree-based methods/GBMs (e.g., H2O, XGBoost, LightGBM); familiarity with clustering, Bayesian methods, regularization, and optimization is a plus

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Keywords to mirror

machine learningaiagilemlpythonsqlaws

Platform: adp-recruiting
Effort: medium

Benefits

Benefits detected in the listing.

Health Insurance

Dental

Vision

401(K)

Paid Time Off

Parental Leave

Wellness

Professional Development