Sr, Data Scientist - Operations Research

Required Skills & Experience

• Bachelor's Degree in Operations Research, Industrial Engineering, Applied Mathematics, Computer Science, or related field or equivalent years of experience
• Master's Degree in Operations Research, Industrial Engineering, Applied Mathematics, Computer Science, or related field or equivalent years of experience
• 3-6 years’ experience in Operations Research / Optimization or related fields preferred
• Hands-on experience with Gurobi (strongly preferred) or similar solvers (CPLEX, OR-Tools, etc.)
• Proven experience building and deploying large-scale mathematical models (LP, MILP, etc.) in production environments
• Ability to design and maintain data pipelines for large-scale modeling workflows and experience working with large, complex datasets, including feature engineering and data validation
• Proficiency in Python and SQL, with emphasis on scalable, maintainable code
• Familiarity with cloud environments (AWS, Azure, GCP) for scalable execution

Job Description

• Formulate decision problems as mathematical optimization models (LP, MILP, MINLP), translating business constraints and objectives into structured frameworks
• Design and implement large-scale optimization models, solving complex problems involving thousands to millions of variables and constraints
• Leverage solvers such as Gurobi (preferred) or equivalent tools to build high-performance solutions, including model tuning and performance optimization
• Develop end-to-end decision systems, integrating optimization outputs into production workflows and business processes
• Collaborate with business stakeholders to define objective functions, constraints, and trade-offs, ensuring solutions align with real-world decision-making
• Evaluate solution quality through scenario testing, sensitivity analysis, and stress testing of optimization results
• Scale and productionize models, including API development, automation, monitoring, and retraining workflows
• Build and maintain data pipelines to support optimization models, ensuring data quality, availability, and performance at scale
• Communicate model structure, assumptions, and results clearly to leadership, translating complex math into actionable business insights