Remote OR Scientist - Supply Chain Optimization

Cognizant; United States; On-site; USD 120,000 - 170,000; Full time

Benefits offered by this job

Remote work

Job summary

Cognizant is seeking an Operations Research Scientist / Optimization Engineer to architect and productionize algorithmic solutions for enterprise-scale retail operations, including MILP, nonlinear, and stochastic models for omnichannel supply chains and routing. You'll bridge theory with cloud data warehousing and AI platforms, collaborating with MLOps, Data Engineering, and product teams to deploy low-latency pipelines.

PhD or MS with strong research/internship backgrounds welcomed.

Qualifications

• Ph.D. or M.Tech / M.S. in Operations Research, Industrial Engineering, Applied Mathematics, Computer Science, Systems Engineering, or related quantitative field.
• Recent Ph.D. graduates with thesis focus on mathematical programming / OR or 1+ years relevant industry/internship experience.
• 2+ years of industry experience applying optimization models in supply chain, logistics, or retail (strong graduate thesis/internship portfolios for new graduates).
• Deep hands-on experience with commercial and open-source solvers (Gurobi, CPLEX, FICO Xpress, OR-Tools, SCIP, PuLP, Pyomo).
• Advanced proficiency in Python (NumPy, SciPy, Pandas, NetworkX) or C++/Julia/Java.
• Solid foundation in LP, MILP, decomposition techniques (Benders, Dantzig-Wolfe, Column Generation), meta-heuristics, and graph theory.

Responsibilities

• Mathematical Formulation & Optimization: Formulate and solve large-scale MILP, nonlinear, stochastic, and dynamic network optimization models.
• Retail & Omnichannel Supply Chain: Design algorithmic solutions for safety stock sizing, forward deployment, store replenishment, omnichannel sourcing, and cross-dock scheduling.
• AI & Data Science Synergy: Develop hybrid models that fuse ML/DL with deterministic/stochastic optimization solvers.
• Simulation & Scenario Modeling: Build discrete-event simulation engines and digital twins to stress-test network resilience and seasonal volatility.
• Production Deployment & Scaling: Partner with MLOps, Data Engineering, and Product teams to scale algorithms into low-latency production pipelines and microservices on cloud.
• Cross-Functional Collaboration: Translate constraints from Merchandising, Store Ops, Logistics, and Finance into mathematical constraints and decision engines.

Skills

Mathematical optimization

Quantitative analysis

Python programming

Team collaboration

Education

Ph.D. in Operations Research

M.Tech / M.S. in OR / Industrial Engineering / Applied Mathematics / Computer Science

Tools

Gurobi

CPLEX

FICO Xpress

Google OR-Tools

SCIP

PuLP

Pyomo

Python

C++/Julia/Java

Job description

Cognizant is seeking an Operations Research Scientist / Optimization Engineer to architect and productionize algorithmic solutions for enterprise-scale retail operations, including MILP, nonlinear, and stochastic models for omnichannel supply chains and routing. You'll bridge theory with cloud data warehousing and AI platforms, collaborating with MLOps, Data Engineering, and product teams to deploy low-latency pipelines.

PhD or MS with strong research/internship backgrounds welcomed.