Operations Research Analyst

Role Overview: As a Data Scientist specializing in operations research and analytics, your main responsibility will be to collaborate with cross-functional teams to define business problems and translate them into quantitative models such as optimization, simulation, and forecasting. You will be expected to extract, clean, and analyze large datasets to identify key drivers, bottlenecks, and opportunities for process improvement. Developing mathematical/OR models like linear programming, integer programming, and simulation to support decision-making will be a crucial part of your role. Additionally, you will implement and validate model prototypes, conduct what-if or sensitivity analyses, and present actionable recommendations to stakeholders. Monitoring model performance, tracking outcomes, and refining models iteratively will also be part of your responsibilities. Communication of complex analytical results in clear, non-technical language and creating dashboards or visualizations to track key metrics and business impact will be essential tasks. Key Responsibilities: - Collaborate with cross-functional teams to define business problems and translate them into quantitative models (e.g., optimization, simulation, forecasting). - Extract, clean, and analyze large datasets; identify key drivers, bottlenecks, and opportunities for process improvement. - Develop mathematical/OR models (e.g., linear programming, integer programming, simulation) to support decision-making (resource allocation, scheduling, supply-chain, manufacturing). - Implement and validate model prototypes; run what-if or sensitivity analyses and present actionable recommendations to stakeholders. - Monitor model performance, track outcomes, and iteratively refine models; document assumptions, methods, and results for business users. - Communicate complex analytical results in clear, non-technical language; create dashboards or visualizations to track key metrics and business impact. Qualifications Required: - Master's or Bachelor's in Operations Research, Industrial Engineering, Applied Math, Statistics, Computer Science, or related field. - 2-4 years of experience in analytics, operations research, or optimization. - Solid programming/statistical skills in Python, R, MATLAB, or similar. - Experience with optimization solvers/tools (e.g., CPLEX, Gurobi, or open-source equivalents) and simulation packages. - Strong SQL/database skills; experience working with large datasets and extracting insights. - Good understanding of operations research techniques (e.g., linear/integer programming, simulation, queues, network flows). - Ability to translate business questions into modeling frameworks and present results credibly to business and technical stakeholders. - Comfortable working in a dynamic environment, collaborating across teams, and managing ambiguity. - Experience in manufacturing/operations/supply-chain domain is a plus. - Prior exposure to dashboarding tools (e.g., PowerBI, Tableau) will be beneficial. - Some exposure to end-to-end solution deployment (data through to insights) would be valued. (Note: The job description does not include any additional details about the company.),