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About This Role
Overview:
Join an amazing team that is consistently recognized for our achievements and culture, including our most recent Forbes award of being one of America's Best Midsize Employers for 2026!
Position Summary:
We are looking for an experienced Lead Data Scientist to join our Growth Data Science team at Mercury Insurance. In this role, you will partner closely with our Marketing, Sales, and Customer Experience organizations to identify growth opportunities, optimize acquisition and engagement strategies, and deliver scalable, data\-driven solutions. You will act as a thought leader—bring deep technical expertise, strong business intuition, and the ability to drive measurable business value through automated, domain\-specific AI solutions.
Geo\-Salary Information:
*An in\-person interview may be required during the hiring process*
State specific pay scales for this role are as follows:
$118,664 to $230,619 (NJ, NY, WA, HI, AK, MD, CT, RI, MA)
$107,876 to $209,653 (NV, OR, AZ, CO, WY, TX, ND, MN, MO, IL, WI, FL, GA, MI, OH, VA, PA, DE, VT, NH, ME)
$97,089 to $188,688 (UT, ID, MT, NM, SD, NE, KS, OK, IA, AR, LA, MS, AL, TN, KY, IN, SC, NC, WV)
In CA: Typical hiring range is $157,177\.35 to $218,301\.88
The expected base salary for this position will vary depending on a number of factors, including relevant experience, skills and location.
Responsibilities:
Essential Job Functions:
- Work directly with Marketing, Sales, and CX leaders to diagnose business challenges, define analytical roadmaps, and drive growth strategies.
- Develop rigorous analyses, models, and frameworks to optimize customer acquisition, lifecycle engagement, and retention.
- Build scalable machine learning solutions (e.g., targeting, segmentation, uplift modeling, LTV prediction) that improve growth efficiency and ROI.
- Drive acquisition, retention, and monetization through data\-driven experimentation and modeling.
- Translate complex data insights into clear, actionable recommendations that influence decision\-making at all levels of the organization.
- Collaborate with engineering teams to productionize models and analytics pipelines with high reliability and performance.
- Familiar with state of art AI solutions in Data Science domain and mentor Jr. data scientists.
- Drive best practices in experimentation, measurement, and growth analytics across the company.
Qualifications:
Education:
Minimum:
- Bachelor's degree in Computer Engineering, Computer Science, Mathematics, Electrical Engineering, Information Systems, or related technical field Or equivalent combination of education and/or experience
Preferred:
- MS/Ph.D. is preferred.
Experience:
Minimum:
- 7\+ years of experience in data science, machine learning, or growth analytics, with a track record of delivering business impact.
Preferred:
- 5 years of experience in data mining and data/statistical analysis.
- 5 years experience with SQL and statistical software packages (Python, R, SAS) required.
- 5 years experience working as a data scientist or similar role, with proven track record in data processing, predictive modeling, and machine learning techniques
- 1 year experience in P\&C insurance
- 1 year experience leading projects in group settings and mentoring peers/interns
Knowledge and Skills:
- Expert\-level SQL and Python, and experience working in modern data and ML ecosystems.
- Experiences in growth\-focused data science (e.g., paid marketing optimization, attribution, funnel diagnostics, experimentation, user segmentation).
- Design and lead complex A/B/n testing frameworks, including multi\-armed bandits and causal inference models to measure impact in ambiguous environments.
- Hands\-on experience building and deploying production ML models.
- Experience in fine\-tuning Generative AI (LLMs) for domain\-specific applications is a plus.
- Solid communication skills—able to simplify complex concepts for non\-technical partners and good storytelling skills.
- Ability to operate in "zero\-to\-one" problem spaces where clear ground truths may not exist.
- Strong product and business intuition; comfortable partnering with senior\-level stakeholders.
- Experience in a consumer, fintech, SaaS, or marketplace environment is a plus.
About the Company:
*Why choose a career at Mercury?*
At Mercury, we have been guided by our purpose to help people reduce risk and overcome unexpected events for more than 60 years. We are one team with a common goal to help others. Everyone needs insurance and we can’t imagine a world without it.
Our team will encourage you to grow, make time to have fun, and work together to make great things happen. We embrace the strengths and values of each team member. We believe in having diverse perspectives where everyone is included, to serve customers from all walks of life.
We care about our people, and we mean it. We reward our talented professionals with a competitive salary, bonus potential, and a variety of benefits to help our team members reach their health, retirement, and professional goals.
Learn more about us here: https://www.mercuryinsurance.com/about/careers
Perks and Benefits:
*We offer many great benefits, including:** Competitive compensation
- Flexibility to work from anywhere in the United States for most positions
- Paid time off (vacation time, sick time, 9 paid Company holidays, volunteer hours)
- Incentive bonus programs (potential for holiday bonus, referral bonus, and performance\-based bonus)
- Medical, dental, vision, life, and pet insurance
- 401 (k) retirement savings plan with company match
- Engaging work environment
- Promotional opportunities
- Education assistance
- Professional and personal development opportunities
- Company recognition program
- Health and wellbeing resources, including free mental wellbeing therapy/coaching sessions, child and eldercare resources, and more
Mercury Insurance is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other characteristic protected by federal, state, or local law.
Pay Range: USD $118,664\.00 \- USD $230,619\.00 /Yr.
Salary Context
This $97K-$230K range is above the median 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 Mercury Insurance Company, 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. This role's midpoint ($163K) sits 15% below the category median. Disclosed range: $97K to $230K.
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.
Mercury Insurance Company AI Hiring
Mercury Insurance Company has 1 open AI role right now. They're hiring across Data Scientist. Based in Remote, US. Compensation range: $230K - $230K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% 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 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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