Overview:
Prodapt is the largest specialized player in the Connectedness industry. As an AI-first strategic technology partner, Prodapt provides consulting, business reengineering, and managed services for the largest telecom and tech enterprises building networks and digital experiences of tomorrow. A ServiceNow-invested company, Prodapt has been recognized by Gartner as a Large, Telecom-Native, Regional IT Service Provider. A “Great Place To Work® Certified” company, Prodapt employs over 6,000 technology and domain experts across the Americas, Europe, India, Africa, & Japan. Prodapt is part of the 130-year-old business conglomerate The Jhaver Group, which employs over 32,000 people across 80+ locations globally.
We are seeking an experienced Senior Applied Scientist (ORSA) with deep expertise in optimization, mathematical modeling, and retail supply chain operations to drive large-scale inventory allocation and replenishment strategies.
Responsibilities:
Lead the optimization architecture: problem formulation, solver selection, constraint modeling
Own the mathematical framework generating optimized allocation plans incorporating business constraints
Architect multi-objective optimization balancing in-stock rates, inventory turns, and operational costs
Guide the optimization team on problem decomposition and solution approach per region
Interface with customer supply chain planning teams on constraint definition and business rule translation
Requirements:
Operations Research (MS or PhD preferred), strong optimization foundation
Bachelor’s degree in Computer Science, Engineering, or a related field; OR equivalent combination of education and relevant experience.
Hands-on 7+ years of experience as Applied Scientist with expertise in Operations Research & Systems Analysis.
Solver experience: Gurobi, CPLEX, or equivalent
Translating business constraints into mathematical formulations
Python, advanced numerical computing
Experience leading or mentoring applied scientists
Retail supply chain or inventory optimization domain knowledge
Preferred:
Store allocation / replenishment system experience
Familiarity with agentic AI frameworks
AWS data/ML stack (SageMaker, Step Functions, Lambda)