Data Scientist - Optimization and Advanced Machine Learning

• *Job Description** Data Scientist - Optimization and Advanced Machine Learning • *FM • Johnston, RI • On-site, Remote • *Salary: $103.04K - $148.10K/yr • *Type:** Full-time • *Benefits:** Medical, Retirement • *Posted:** 3 hours ago • *Job Description** FM is seeking a Data Scientist specializing in optimization and advanced machine learning to translate business needs into analytical & AI solutions. The role involves designing, developing, and deploying optimization models, AI/ML algorithms, and decision‑support systems for real‑world problems across various teams. Key Responsibilities • Build machine learning and AI solutions using Python/R and modern ML frameworks. • Develop and deploy optimization models 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. • *Qualifications • *Education: • Master’s degree in Operations Research, Industrial Engineering, Applied Mathematics, Computer Science, Data Science, or a related field. 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. • Experience leading full‑cycle data science or optimization projects from ideation to deployment. • 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 • Metaheuristics (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.