Operations Research Scientist, AI Labs

About Adani Group

Adani Group is a diversified organization in India, comprising 10 publicly traded companies. It has established a world-class logistics and utility infrastructure portfolio with a pan-India presence. Headquartered in Ahmedabad, Gujarat, India, Adani Group has emerged as a market leader in its logistics and energy businesses, focusing on large-scale infrastructure development with globally benchmarked O&M practices. With four IG-rated businesses, it stands as the sole Infrastructure Investment Grade issuer in India.

Job Purpose: Operations Research Scientist, AI Labs

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

Responsibilities

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

External:

• Technology Vendors & AI Solution Providers
• Research Institutions & Academia
• Regulatory Authorities & Compliance Bodies

Qualifications

Educational Qualification:

• Ph.D. or Master’s degree in Operations Research, Industrial Engineering, Statistics, Applied Mathematics, Computer Science, or related fields.

Certifications:

• 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:

• 2-5 years of experience in developing and deploying operations research models in engineering, logistics, manufacturing, or supply chain domains.

Key skills/competency

• Operations Research
• Optimization Models
• Mathematical Optimization
• AI/ML Techniques
• Supply Chain Optimization
• Logistics Optimization
• Algorithm Development
• Data-driven Decision Making
• Simulation Modeling
• Cloud Platforms (AWS, Google, Azure)