Data Scientist III - Insurance & Generative AI (Hybrid- Webster or Boston)
MAPFREAbout the role
Summary
We are looking for a forward-thinking analytics leader to design, build, and scale advanced analytical solutions that solve complex, high-impact business problems. This role combines deep technical expertise in predictive modeling and machine learning with strong business acumen and insurance domain knowledge.
You will play a critical role in shaping strategy by delivering actionable insights, presenting to senior leadership, and owning the long-term success of models and analytics products. You’ll also help drive innovation through the application of cutting-edge techniques, including Generative AI, while mentoring others and advancing best practices across the organization.
What You’ll Do
- Lead the development and deployment of advanced predictive models, machine learning solutions, and Generative AI use cases for complex business challenges
- Translate analytics into actionable insights that drive decision-making and align with company strategy
- Build and automate sophisticated reports, dashboards, and model outputs for business and executive stakeholders
- Present model performance, lift, and impact analyses to senior leadership
- Own the full lifecycle of models, including development, validation, deployment, monitoring, and continuous improvement
- Conduct research into innovative algorithms and GenAI approaches to unlock new opportunities and use cases
- Develop, test, and refine prompts, and support the design of agent-based GenAI workflows
- Work with complex datasets, including sparse, high-dimensional, and time-series data
- 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
- 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
- Lead end-to-end project execution, prioritizing high-impact initiatives and managing timelines
- 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
- Deep expertise in predictive modeling, machine learning, and statistical analysis
- 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 Generative AI, including prompt engineering, testing/refinement, and agent-based solution design
- 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
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