Operations Research Scientist-AI Labs

Purpose/Objective

The Operations Research Scientist will be responsible for designing, developing, and implementing optimization and simulation models that drive data-informed decision-making across key business functions. The role will focus on leveraging advanced mathematical, statistical, and AI techniques to optimize operations in ports, logistics, transportation, supply chain, demand planning, manufacturing, and sourcing. The incumbent will play a key role in enhancing efficiency, reducing costs, and enabling long-term strategic and tactical business decisions across Adani Group companies.

Key Responsibilities of Role

Operations Research Scientist-AI Labs Development & Implementation of Optimization Models: Improve operational efficiency and cost-effectiveness by developing and implementing optimization models for logistics, supply chain, manufacturing, and other business processes. Enhance business decision-making by applying mathematical optimization techniques such as linear programming, combinatorial optimization, integer programming, and network flow algorithms. Ensure scalability and adaptability of models by leveraging advanced AI ML methodologies, hybrid optimization techniques, and data-driven simulation models. Optimize business strategies by integrating optimization models into long-term strategic planning and short-term tactical decision-making. Advanced Algorithm Development & Problem Solving: Enhance model performance and accuracy by leveraging simulated annealing, genetic algorithms, tabu search, Markov decision processes, and ant colony optimization. Drive process automation and optimization by designing data-driven decision support systems to optimize business workflows. Optimize EV fleet operations by designing data-driven charging optimization models, ensuring cost-effective and efficient EV charging strategies. Enhance cost reduction in logistics by applying combinatorial optimization, network flow algorithms, and machine learning-driven route planning. Reduce complexity and improve computational efficiency by implementing decomposition algorithms and large-scale mathematical models. Business Problem-Solving with Operations Research: Enable predictive and prescriptive decision-making by analyzing historical and real-time data to develop optimal operational strategies. Minimize operational disruptions and enhance resource utilization by applying discrete event simulation techniques. Improve cost efficiency and risk mitigation by integrating optimization models with business forecasting and financial planning. Cross-Functional Collaboration & AI Integration: Ensure alignment of optimization models with business objectives by collaborating with business leaders, strategy teams, and data scientists. Drive AI adoption in operations research by working with IT and engineering teams to deploy models on scalable cloud-based platforms. Translate complex mathematical models into actionable insights by effectively communicating findings to C-level executives and decision-makers. AI Governance, Compliance & Continuous Improvement: Ensure regulatory compliance and ethical AI implementation by developing governance frameworks for AI-driven optimization models. Improve model robustness and reliability by conducting regular model validation, risk assessment, and algorithm tuning. Drive continuous innovation in operations research by staying updated with emerging AI trends, algorithmic advancements, and best practices in mathematical optimization. Key Stakeholders - Internal Business Strategy & Operations Teams Data Science & AI Teams IT & Cloud Engineering Teams Finance & Risk Management Teams Key Stakeholders - External Technology Vendors & AI Solution Providers Research Institutions & Academia Regulatory Authorities & Compliance Bodies

Technical Competencies

AI-Driven Enterprise Transformation & Operational Excellence-SDIL,Data Monetization & Strategic Analytics-SDIL,Machine Learning & AI Integration-SDIL,Operational Strategy, Optimization & Decision Science-SDIL,Predictive Analytics & Business Intelligence-SDIL,Public Policy & Regulatory Strategy-SDIL,Strategic Business Development & Planning-SDIL

Qualifications and Experience

Educational Qualification: Ph.D. or Master’s degree in Operations Research, Industrial Engineering, Statistics, Applied Mathematics, Computer Science, or related fields. Certification: Certified Optimization & OR Specialist (CPLEX Gurobi Certification) Advanced AI & ML Certification (Google AWS Microsoft AI) Data Science & Statistical Analysis Certification (Coursera IBM DataCamp) Work Experience (Range of years): 2-5 years of experience in developing and deploying operations research models in engineering, logistics, manufacturing, or supply chain domains.