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About This Role
This position is not eligable for sponsorship now or in the future.
Summary
We are looking for a forward-thinking analytics leader to design, build, and scale analytical solutions that solve complex, high-impact business problems. This role requires strong technical depth in machine learning, Python, and SQL, along with business acumen and insurance domain knowledge.
The successful candidate will take ownership of problems from discovery through implementation, develop a deep understanding of complex data systems, look beyond surface-level results, proactively identify and work through blockers, and communicate clearly with team members and stakeholders. Familiarity with Generative AI concepts, prompt engineering practices, and emerging agentic AI approaches is a big plus.
What You’ll Do
- Lead the development and deployment of advanced predictive models and machine learning solutions for complex business challenges
- Identify data misalignments, investigate root causes and develop targeted solutions to improve model performance. Proactively address blockers, evaluate alternatives, and work with the appropriate partners to move solutions forward
- Apply strong Python and SQL skills to explore data, engineer features, build models, automate workflows, and validate results
- Measure economic impact and partner with business teams to define KPIs and ensure value realization
- Design processes and tools to monitor model performance, reliability, and stability in production
- Present model performance, business impact, and analytical solutions to senior leadership
- Develop, test, and refine prompts, and support the design of agent-based GenAI workflows
- Conduct research into innovative algorithms and GenAI approaches to unlock new opportunities and use cases
- Develop internal tools and programs to accelerate deployment, retraining, and scaling of models (MLOps), supporting both batch and real-time environments
- Collaborate cross-functionally and serve as the key liaison with IT to implement and scale solutions
- Create executive-ready presentations and detailed technical documentation to communicate results and best practices
- Mentor and guide junior team members while promoting best-in-class modeling and statistical techniques
- Partner with data governance and business teams to improve data quality, feature engineering, and overall data strategy
- Identify and evaluate new data sources and emerging analytical techniques to maintain competitive advantage
- Advocate for a data-driven culture and continuous innovation across the organization
What You’ll Bring
- Bachelor’s degree in Statistics, Mathematics, Data Science, Economics, Finance, Engineering, or a related quantitative field (required), with either 8+ years of relevant experience, or a Master’s degree with 2+ years of relevant experience
- Must have Property & Casualty Insurance experience
- A results-driven mindset with the persistence to dig deeply into problems, identify root causes, and work proactively toward solutions
- Deep expertise in predictive modeling, machine learning, and statistical analysis and technical details required to build reliable solutions
- Strong experience working with complex data structures (sparse, high-dimensional, and time-series data)
- Solid insurance domain knowledge is required, with claims experience highly preferred
- Experience with prompt optimization, evaluation and refinement
- Proven ability to solve ambiguous, complex problems with minimal direction and high autonomy
- Strong communication skills, with the ability to translate complex analytics into clear business insights for senior stakeholders
- Experience owning and managing models in production environments, including monitoring and MLOps practices
- Demonstrated ability to innovate, research new techniques, and apply them to real-world business problems
- Experience mentoring team members and influencing best practices across teams
- Strong organizational and project management skills, with the ability to prioritize high-value work
Why Mapfre?
As a global insurance leader with a strong local presence, we offer more than a job — we provide a purpose-driven career where your growth, well-being, and impact truly matter.
- Purpose & Culture: Join a company built on trust, collaboration, and inclusion. Our values guide everything we do, creating a workplace where people feel respected and empowered.
- Comprehensive Benefits: Enjoy competitive health coverage, retirement plans, paid time off, flexible work options, and lifestyle perks like employee discounts.
- Career Growth: Advance your skills through tuition reimbursement, leadership programs, and internal mobility opportunities. Your development is our priority.
- Social Responsibility: Contribute to meaningful initiatives through Fundación Mapfre, supporting communities and sustainability worldwide.
Pay Transparency: The typical starting salary range for this role is determined by several factors including skills, experience, education, certifications, and location. Some roles at Mapfre are eligible for commission and/or bonus earnings, in addition to salary, calculated based upon factors set forth in the compensation plan for the role.
Salary Range: $130,000 - $160,000
Benefits Highlights:
At Mapfre, we invest in our employees' well-being by offering a comprehensive benefits package, including:
- Paid Time Off (PTO): A flexible PTO program that combines vacation, personal, volunteer and sick time into one convenient bank of paid time off.
- 401(k) & Profit Sharing: Eligible employees may participate in Mapfre's 401(k) plan that includes company matching contributions up to certain amounts under the terms of the plan.
- Medical, Dental and Vision Coverage: Choice of comprehensive medical, dental and vision insurance coverage under group plans offered by the company.
- Other Benefits Include: STD, LTD, Life Insurance, FSA, HSA and various wellness programs
Benefits provided are subject to the terms and conditions of the applicable benefit plan or policy. The Company reserves the right to alter, amend, or terminate benefits in its sole discretion. To learn more about our benefit offerings please visit:https://mapfre.gobenefits.net/
If you require a reasonable accommodation during the application or selection process, please contact the Mapfre Talent Acquisition team at [email protected]. Mapfre is proud to be an Equal Opportunity Employer.
Salary Context
This $130K-$160K range is below the median for Data Scientist roles in our dataset (median: $167K across 150 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 8,943 AI roles we're tracking, Data Scientist positions make up 4% of the market. At MAPFRE Insurance, 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 $190,000 based on 1,406 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($145K) sits 24% below the category median. Disclosed range: $130K to $160K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
MAPFRE Insurance AI Hiring
MAPFRE Insurance has 1 open AI role right now. They're hiring across Data Scientist. Based in Webster, MA, US. Compensation range: $160K - $160K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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