Data Scientist - Optimization and Advanced Machine Learning

Established nearly two centuries ago, FM is a leading mutual insurance company whose capital, scientific research capability and engineering expertise are solely dedicated to property risk management and the resilience of its policyholder-owners. These owners, who share the belief that the majority of property loss is preventable, represent many of the world’s largest organizations, including one of every four Fortune 500 companies.

They work with FM to better understand the hazards that can impact their business continuity to make cost-effective risk management decisions, combining property loss prevention with insurance protection.

As a Data Scientist specializing in optimization and advanced machine learning
, you will translate business needs into analytical & AI solutions that drive FM’s mission of loss prevention and risk reduction. You will design, develop, and deploy optimization models, AI/ML algorithms, and decision‑support systems for real‑world, large‑scale problems across underwriting, engineering, operations, and innovation teams.
You will work in a collaborative, forward‑thinking analytics environment, leveraging modern tools and technologies to deliver impactful solutions.

Key Responsibilities

• Build machine learning and AI solutions using Python/R and modern ML frameworks for prediction, clustering, simulation, and automated decision systems.
• Develop and deploy optimization models (e.g., linear, nonlinear, mixed‑integer programming, stochastic optimization) for risk assessment and operational decision‑making.
• Lead end‑to‑end model development: problem framing, data processing, feature engineering, modeling, validation, implementation, and monitoring.
• Work with cross‑functional partners to translate analytical insights into business actions.
• Work with large‑scale datasets using SQL or similar tools and implement robust data pipelines.
• Apply creativity and domain expertise to evaluate and improve model performance and interpretability.
• Contribute to continuous innovation in optimization, ML, and AI practices across the organization.

Education (minimum requirement)

• Master’s degree in Operations Research, Industrial Engineering, Applied Mathematics, Computer Science, Data Science, or a closely related field with strong optimization focus. PhD preferred.

Experience

• 5+ years of industry experience in building optimization or machine learning solutions.
• 5+ years hands‑on experience with data processing, modeling, and advanced analytics using Python or R.
• 3+ years experience working with large datasets using SQL or similar technologies.
• Proven experience leading full‑cycle data science or optimization projects from ideation to deployment.
• Hands‑on experience with cloud‑based analytics and machine learning platforms, preferably Azure Databricks

Technical Skills

Strong Knowledge & Practical Experience in:

• Optimization methods:
  • Linear / Non‑linear programming
  • Mixed‑integer optimization
  • Meta heuristics (GA, simulated annealing, evolutionary algorithms)
  • Stochastic and robust optimization
• Core Machine Learning algorithms:
  • Tree‑based models, gradient boosting
  • Clustering & unsupervised learning
  • Neural networks and deep learning fundamentals
• Simulation
• Model evaluation, validation, and experimentation
• Data wrangling, feature engineering, and pipeline design
• ML engineering practices following MLOps principles (model tracking, reproducibility, deployment)

Preferred Qualifications

• Experience in risk management
  , insurance
  , or operations modeling
  .

The final salary offer will vary based on geographic location, individual education, skills, and experience. The position is eligible to participate in FM's comprehensive Total Rewards program that includes an incentive plan, generous health and well-being programs, a 401(k) and pension plan, career development opportunities, tuition reimbursement, flexible work, time off allowances and much more.

FM is an Equal Opportunity Employer and is committed to attracting, developing, and retaining a diverse workforce.