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About This Role
For more than 170 years, The Hanover has been committed to delivering on our promises and being there when it matters the most. We live our values every day, demonstrating we CARE through our values, Sustainability initiatives and inclusive corporate culture.
Our Personal Lines team is currently seeking a Data Scientist in our Worcester, MA office on a hybrid arrangement or fully remote work location. This is a full\-time, exempt role.
Join a Team Where Data Drives Strategy:
At The Hanover, data science is a key driver of how we understand risk, improve customer experiences, and make smarter business decisions. We're looking for a curious, analytical, and collaborative Data Scientist to join our Personal Lines Analytics team.
In this role, you'll apply advanced analytics, machine learning, and statistical modeling techniques to solve meaningful business challenges across our Personal Lines organization. You'll work alongside business leaders, product managers, actuaries, and data scientists to transform complex data into actionable insights that influence underwriting, pricing, customer experience, and growth strategies.
This is an opportunity to work with large and complex datasets, develop production\-ready analytical solutions, and help shape the future of a data\-driven insurance organization.
Why Join The Hanover?
- Work on high\-impact analytics projects that directly influence business strategy and outcomes.
- Partner with experienced data scientists, actuaries, and business leaders across the organization.
- Access large\-scale datasets and real\-world business challenges that offer meaningful analytical opportunities.
- Grow your technical and business skills while developing expertise in predictive analytics and machine learning.
- Be part of a collaborative team that values innovation, continuous learning, and knowledge sharing.
- Help modernize how a leading insurance organization uses data to serve customers and manage risk.
IN THIS ROLE, YOU WILL:
- Develop and deploy predictive models, machine learning solutions, and statistical analyses to solve business problems.
- Explore large datasets to identify patterns, trends, opportunities, and risks that drive business performance.
- Partner with Personal Lines leaders and cross\-functional stakeholders to understand business objectives and translate them into analytic solutions.
- Design experiments, conduct exploratory analyses, and evaluate model performance to generate actionable insights.
- Research and apply emerging data science methods, tools, and technologies to enhance business outcomes.
- Communicate findings and recommendations through compelling visualizations, presentations, and storytelling tailored to technical and non\-technical audiences.
- Collaborate with data engineers and analytics partners to develop scalable, efficient, and sustainable analytical solutions.
- Contribute to a culture of continuous learning, innovation, and analytical excellence.
WHAT YOU NEED TO APPLY:
Required Qualifications
- Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, Analytics, Economics, or a related quantitative field.
- 2\-6 years of experience applying data science, machine learning, advanced analytics, or statistical modeling in a business environment.
- Experience using Python and/or R for data analysis and model development.
- Strong foundation in statistical analysis, predictive modeling, machine learning, and data mining techniques.
- Experience working with large datasets and relational databases.
- Ability to translate business questions into analytical approaches and communicate results effectively.
- Strong problem\-solving skills and intellectual curiosity.
Preferred Qualifications
- Master's degree in Data Science, Statistics, Applied Mathematics, Computer Science, or a related field.
- Experience building and deploying machine learning models in production environments.
- Familiarity with cloud\-based analytics platforms and modern data science workflows.
- Insurance, financial services, or other risk\-based industry experience.
- Experience with data visualization and storytelling tools.
This job posting provides cursory examples of some of the job duties associated with this position. The examples provided are not complete, and the position may entail other essential and job\-related functions and responsibilities that employees will be required to perform.
CAREER DEVELOPMENT:
It’s not just a job, it’s a career, and we are here to support you every step of the way. We want you to be successful and fulfilled. Through on\-the\-job experiences, personalized coaching and our robust learning and development programs, we encourage you – at every level – to grow and develop.
BENEFITS:
We offer comprehensive benefits to help you be healthy, build financial security, and balance work and home life. At The Hanover, you’ll enjoy what you do and have the support you need to succeed.
Benefits include:
- Medical, dental, vision, life, and disability insurance
- 401K with a company match
- Tuition reimbursement
- PTO
- Company paid holidays
- Flexible work arrangements
- Cultural Awareness Day in support of IDE
- On\-site medical/wellness center (Worcester only)
- Click here for the full list of Benefits
EEO statement:
The Hanover values diversity in the workplace and among our customers. The company provides equal opportunity for employment and promotion to all qualified employees and applicants on the basis of experience, training, education, and ability to do the available work without regard to race, religion, color, age, sex/gender, sexual orientation, national origin, gender identity, disability, marital status, veteran status, genetic information, ancestry or any other status protected by law.
Furthermore, The Hanover Insurance Group is committed to providing an equal opportunity workplace that is free of discrimination and harassment based on national origin, race, color, religion, gender, ancestry, age, sexual orientation, gender identity, disability, marital status, veteran status, genetic information or any other status protected by law.”
As an equal opportunity employer, Hanover does not discriminate against qualified individuals with disabilities. Individuals with disabilities who wish to request a reasonable accommodation to participate in the job application or interview process, or to perform essential job functions, should contact us at: [email protected] and include the link of the job posting in which you are interested.
Privacy Policy:
To view our privacy policy and online privacy statement, click here.
Applicants who are California residents: To see the types of information we may collect from applicants and employees and how we use it, please click here.
Compensation:
The target hiring range for this role may vary based on geographic location and other factors, including merit or performance, demonstrated proficiency, skills for the role, education, travel requirements, and experience. Additional compensation may include an annual bonus (which could take the form of a general bonus, sales incentive, or short\-term incentive), long\-term incentive or spot recognition awards. The posted range reflects our ability to hire at different position titles and levels depending on background and experience.
Salary Context
This $85K-$105K range is in the lower quartile for Data Scientist roles in our dataset (median: $160K across 258 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 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At The Hanover Insurance Group, 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 789 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($95K) sits 51% below the category median. Disclosed range: $85K to $105K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
The Hanover Insurance Group AI Hiring
The Hanover Insurance Group has 1 open AI role right now. They're hiring across Data Scientist. Based in Remote, US. Compensation range: $105K - $105K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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