Senior Data Scientist - Insurance & GenAI Analytics

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

Benefits offered by this job

Paid Time Off

401(k) with company match

Medical, dental and vision coverage

Wellness programs

Job summary

MAPFRE, a global insurance leader, seeks a Data Scientist III to design, build, and scale analytical solutions in a hybrid role based in Webster, MA or Boston. You will apply machine learning, Python, and SQL to solve high-impact business problems in insurance.

You will own problems from discovery through implementation, mentor juniors, and partner with IT to scale embeddings, models, and MLOps pipelines for batch and real-time environments.

Qualifications

• Bachelor's degree in Statistics, Mathematics, Data Science, Economics, Finance, Engineering, or related field.
• 8+ years of experience or a Master's with 2+ years in analytics.
• Property & Casualty Insurance experience required.
• Able to design and implement predictive models with strong statistical grounding.

Responsibilities

• Lead development and deployment of advanced predictive models for insurance business challenges.
• Apply Python and SQL to explore data, engineer features, and validate results.
• Measure business impact, define KPIs, and ensure value realization.
• Mentor junior teammates and collaborate with IT to scale solutions.
• Develop GenAI prompts and assist in agent-based GenAI workflows.

Skills

Machine learning

Python

SQL

Insurance domain knowledge

Data analytics

Team mentorship

Communication

Education

Bachelor's degree in a quantitative field

Master's degree in a related field

Tools

ML frameworks (TensorFlow/PyTorch)

MLOps tooling

Data visualization tools

Job description

MAPFRE, a global insurance leader, seeks a Data Scientist III to design, build, and scale analytical solutions in a hybrid role based in Webster, MA or Boston. You will apply machine learning, Python, and SQL to solve high-impact business problems in insurance.

You will own problems from discovery through implementation, mentor juniors, and partner with IT to scale embeddings, models, and MLOps pipelines for batch and real-time environments.