Note: The job is a remote job and is open to candidates in USA. Cognizant is seeking an Operations Research Scientist / Optimization Engineer to develop and productionize algorithmic solutions for enterprise-scale retail operations and supply chain optimization. The role focuses on mathematical programming, AI and data science integration, simulation, cloud deployment, and translating business constraints into optimization models and decision engines.
Responsibilities
- Mathematical Formulation & Optimization: Formulate and solve large-scale Mixed-Integer Linear Programming (MILP), non-linear, stochastic, and dynamic network optimization models
- Retail & Omnichannel Supply Chain: Design algorithmic solutions for safety stock sizing, forward deployment, store replenishment, omnichannel order sourcing, and cross-dock cross-flow scheduling
- AI & Data Science Synergy: Develop hybrid models that fuse Machine Learning/Deep Learning (for demand uncertainty & lead-time forecasting) with deterministic/stochastic optimization solvers
- Simulation & Scenario Modeling: Build discrete-event simulation engines and digital twins to stress-test network resilience, DC automation throughput, and seasonal volatility (e.g., peak holiday surges)
- Production Deployment & Scaling: Partner with MLOps, Data Engineering, and Product teams to scale algorithms into low-latency production pipelines and microservices on cloud infrastructure
- Cross-Functional Collaboration: Translate complex business constraints from Merchandising, Store Operations, Logistics, and Finance into mathematical constraints and executive decision engines
Skills
- • Education: Ph.D. or M.Tech / M.S. in Operations Research, Industrial Engineering, Applied Mathematics, Computer Science, Systems Engineering, or related quantitative field
- • Experience (Ph.D. Track): Recent Ph.D. graduates with thesis focus on mathematical programming / OR or 1+ years relevant industry/internship experience
- • Experience (M.Tech / M.S. Track): 2+ years of industry experience applying optimization models in supply chain, logistics, or retail (strong graduate thesis/internship portfolios considered for recent graduates)
- • Optimization Solvers: Deep hands-on experience with commercial and open-source mathematical solvers (e.g., Gurobi, CPLEX, FICO Xpress, Google OR-Tools, SCIP, PuLP, Pyomo)
- • Core Programming: Advanced proficiency in Python (NumPy, SciPy, Pandas, NetworkX) or C++/Julia/Java
- • Algorithmic Foundation: Solid foundation in LP, MILP, decomposition techniques (Benders, Dantzig-Wolfe, Column Generation), meta-heuristics, and graph theory
Benefits
- Remote work
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