Operations Research & Optimization Scientist

Job Description: Operations Research & Optimization Scientist

Role Overview

We are seeking a skilled Operations Research & Optimization Scientist to design, develop, and deploy advanced mathematical optimization models and analytical solutions that drive business efficiency and support strategic and operational decision-making. The ideal candidate will have strong expertise in mathematical optimization, predictive and prescriptive analytics, simulation, and data-driven problem-solving.

The role involves translating complex business challenges into mathematical models, developing scalable optimization algorithms, and collaborating with cross-functional stakeholders to improve operational performance, optimize resource utilization, and reduce costs.

Key Responsibilities

• Design, develop, and implement optimization models using Linear Programming (LP), Mixed-Integer Programming (MIP), Constraint Programming, Network Optimization, and heuristic/metaheuristic algorithms.
• Develop predictive and prescriptive analytics solutions to support strategic planning, operational decision-making, and business process optimization.
• Build mathematical models for scheduling, routing, inventory optimization, demand planning, production planning, and capacity management.
• Analyze large-scale structured and unstructured datasets to identify operational inefficiencies, cost-reduction opportunities, and process improvements.
• Collaborate with business stakeholders, domain experts, and cross-functional teams to translate business requirements into analytical models and optimization solutions.
• Develop simulation models to evaluate business scenarios, assess risks, and quantify trade-offs under different operating conditions.
• Integrate optimization algorithms and machine learning models into production environments to deliver scalable, reliable, and business-focused solutions.
• Validate model performance, conduct sensitivity and scenario analyses, and continuously improve optimization strategies based on business outcomes.
• Communicate analytical findings, model results, and actionable recommendations to technical and non-technical stakeholders.

Required Qualifications and Skills

• Bachelor's or master's degree in Operations Research, Industrial Engineering, Mathematics, Statistics, Computer Science, Data Science, or a related quantitative field.
• Strong understanding of mathematical optimization, operations research techniques, and algorithm design.
• Hands-on experience with LP, MIP, constraint programming, network flow, and heuristic optimization methods.
• Proficiency in Python and relevant data analysis and scientific computing libraries.
• Experience with optimization solvers and frameworks such as Gurobi, IBM ILOG CPLEX, Google OR-Tools, Pyomo, or PuLP.
• Knowledge of statistical modeling, predictive analytics, machine learning, and simulation techniques.
• Strong analytical, quantitative reasoning, and problem-solving skills.
• Ability to translate complex business problems into mathematical formulations and actionable insights.
• Understanding of model validation, computational performance, scalability, and production deployment practices.

Preferred Qualifications

• Experience solving real-world optimization problems in supply chain, logistics, manufacturing, transportation, workforce planning, or resource allocation.
• Familiarity with cloud platforms, APIs, containerization, and model deployment workflows.
• Experience with SQL, data pipelines, version control, and production-grade analytics systems.
• Knowledge of stochastic optimization, robust optimization, Monte Carlo simulation, and uncertainty modeling.
• Experience working with large datasets and integrating optimization models with machine learning pipelines.

Key Competencies

• Mathematical modeling and algorithm development
• Operations research and decision optimization
• Predictive and prescriptive analytics
• Simulation and scenario analysis
• Data-driven decision-making
• Stakeholder collaboration and communication
• Model deployment, scalability, and performance optimization

Expected Outcomes

• Improved operational efficiency and resource utilization.
• Reduced operating costs and improved service levels.
• Better planning, scheduling, routing, and inventory decisions.
• Increased accuracy and consistency in data-driven decision-making.
• Successful deployment of scalable optimization solutions that deliver measurable business value.