Data Scientist III- Insurance & Advanced Analytics

DataJobs; Webster (MA); Hybrid; USD 130,000 - 160,000; Full time

Benefits offered by this job

PTO

401(k) & Profit Sharing

Medical, Dental and Vision Coverage

STD, LTD, Life Insurance, FSA, HSA and

Job summary

MAPFRE in Webster, MA is seeking a senior data science professional to design, build, and scale advanced predictive models and GenAI workflows for insurance problems. You will own projects end-to-end from discovery through production, collaborating with IT and business teams to realize value and drive data-driven decisions.

The role requires strong Python/SQL skills, production ML experience, and the ability to translate analytics into executive insights while mentoring junior team members.

Qualifications

• Bachelor’s degree in Statistics, Mathematics, Data Science, Economics, Finance, Engineering, or related quantitative field with 8+ years of experience or a Master’s with 2+ years of experience.
• Deep expertise in predictive modeling, machine learning, and statistical analysis.
• Experience owning and managing models in production with MLOps practices.

Responsibilities

• Lead development and deployment of advanced predictive models and ML solutions.
• Design prompts and GenAI workflows and support agent-based GenAI approaches.
• Monitor model performance and reliability in production with MLOps practices.
• Communicate results and business value to senior leadership.
• Collaborate cross-functionally with IT and business teams to scale solutions.

Skills

Insurance domain knowledge

Education

Bachelor’s degree or higher in a quantitative field

Master’s degree with 2+ years of relevant experience

Tools

Python

SQL

Job description

This role focuses on designing, building, and scaling advanced analytical solutions and predictive machine learning models to address complex insurance business problems. The position emphasizes end-to-end ownership from discovery through implementation, including GenAI prompt and agent workflows and production MLOps monitoring.

Key Responsibilities

• Lead the development and deployment of advanced predictive models and machine learning solutions for complex business challenges.
• Detect data misalignments, investigate root causes, and develop targeted improvements to enhance model performance; proactively address blockers and coordinate with relevant partners to move solutions forward.
• Use strong Python and SQL skills for data exploration, feature engineering, model development, workflow automation, and validation.
• Assess economic impact by partnering with business teams to define KPIs and ensure value realization.
• Design processes and tools to monitor model performance, reliability, and stability in production.
• Communicate 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 identify new opportunities and use cases.
• Develop internal tools and programs to accelerate model deployment, retraining, and scaling (MLOps) across both batch and real-time environments.
• Collaborate cross-functionally and act as the key liaison with IT to implement and scale solutions.
• Create executive-ready presentations and detailed technical documentation to share 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.

Required Qualifications

• 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.
• Property & Casualty Insurance experience.
• Results-driven mindset with persistence to dig into problems, identify root causes, and work proactively toward solutions.
• Deep expertise in predictive modeling, machine learning, and statistical analysis, including the technical details needed to deliver reliable solutions.
• Experience working with complex data structures, including sparse, high-dimensional, and time-series data.
• Solid insurance domain knowledge; claims experience is highly preferred.
• Experience with prompt optimization, evaluation, and refinement.
• Proven ability to resolve ambiguous, complex problems with minimal direction and high autonomy.
• Strong communication skills, including 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.

Technologies

• Python
• SQL

Location and Work Style

Webster, MA (hybrid)

Compensation

Salary range: $130,000 - $160,000 per year.

Pay transparency: The typical starting salary range is determined by factors including skills, experience, education, certifications, and location. Some roles at Mapfre may be eligible for commission and/or bonus earnings in addition to salary, based on the compensation plan for the role.

Benefits

• Paid Time Off (PTO)
• 401(k) & Profit Sharing
• Medical, Dental and Vision Coverage
• STD, LTD, Life Insurance, FSA, HSA and various wellness programs
• Tuition reimbursement
• Leadership programs
• Internal mobility opportunities

About MAPFRE

As a global insurance leader with a strong local presence, MAPFRE offers a purpose-driven career focused on growth, well-being, and impact.