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About This Role
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 discipline.
Salary Range:â
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.â
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\-PC1*
Salary Context
This $152K-$217K range is above the 75th percentile for Data Scientist roles in our dataset (median: $155K across 226 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Plymouth Rock Assurance, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 463 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. Disclosed range: $152K to $217K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Plymouth Rock Assurance AI Hiring
Plymouth Rock Assurance has 4 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in Boston, MA, US. Compensation range: $160K - $600K.
Location Context
AI roles in Boston pay a median of $210,000 across 97 tracked positions. That's 3% below the national median.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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