Data Scientist- Insurance & Advanced Analytics (Hybrid- Webster or Boston)

MAPFRE•Webster, MA

1d•$130,000 - $160,000•Hybrid

About The Position

MAPFRE is seeking a forward-thinking analytics leader to design, build, and scale analytical solutions for complex, high-impact business problems. The role requires technical depth in machine learning, Python, and SQL, along with business acumen and Property & Casualty insurance domain knowledge. The successful candidate will own problems from discovery through implementation, develop an understanding of complex data systems, investigate results and blockers, and communicate clearly with team members and stakeholders. Familiarity with Generative AI concepts, prompt engineering, and emerging agentic AI approaches is a plus. The position leads the development and deployment of predictive models and machine learning solutions, supports production monitoring and MLOps for batch and real-time environments, and partners with business, IT, and data governance teams to put solutions into use and improve data quality and strategy. It also includes measuring business impact, communicating findings to senior leaders, researching new algorithms and GenAI approaches, and mentoring junior team members. The role is hybrid and based in Webster or Boston. It is not eligible for sponsorship now or in the future. The typical starting salary range is $130,000–$160,000, with some roles at MAPFRE eligible for commission and/or bonus earnings under the applicable compensation plan.

Requirements

Bachelor’s degree in Statistics, Mathematics, Data Science, Economics, Finance, Engineering, or a related quantitative field, with 8 or more years of relevant experience; alternatively, a master’s degree with 2 or more years of relevant experience.
Property & Casualty insurance experience is required.
Deep expertise in predictive modeling, machine learning, and statistical analysis, including the technical details needed to build reliable solutions.
Strong experience working with complex data structures, including sparse, high-dimensional, and time-series data.
Solid insurance domain knowledge.
Experience with prompt optimization, evaluation, and refinement.
Ability to solve 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.
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, including prioritizing high-value work.
A results-driven mindset and persistence in investigating root causes and working proactively toward solutions.

Nice To Haves

Claims experience is highly preferred.
Familiarity with Generative AI concepts, prompt engineering practices, and emerging agentic AI approaches.

Responsibilities

Lead development and deployment of advanced predictive models and machine learning solutions for complex business challenges.
Identify data misalignments, investigate root causes, develop solutions to improve model performance, address blockers, evaluate alternatives, and work with partners to move solutions forward.
Use Python and SQL 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.
Research innovative algorithms and GenAI approaches to identify new opportunities and use cases.
Develop internal tools and programs to accelerate model deployment, retraining, and scaling through MLOps, supporting 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 strong modeling and statistical techniques.
Partner with data governance and business teams to improve data quality, feature engineering, and data strategy.
Identify and evaluate new data sources and emerging analytical techniques.
Advocate for a data-driven culture and continuous innovation across the organization.