Associate Lead Data Scientist - Operations Research & Optimization

We are looking for an experienced Associate Lead Data Scientist (ALDS) with a strong foundation in Machine Learning, Operations Research, Optimization, and Advanced Analytics . The ideal candidate should have hands-on experience solving complex business problems using optimization techniques such as Mixed Integer Linear Programming (MILP), Constrained Programming, and Non-Linear Programming , along with expertise in modern machine learning and cloud technologies.

This role involves a combination of hands-on technical contribution, client engagement, project ownership, team mentoring, and solution design across domains such as Retail, Consumer Goods, Pharma, Banking, and Insurance .

Candidates with experience in Trade Promotion Optimization (TPO) , Revenue Growth Management (RGM) , pricing optimization, supply chain optimization, or commercial analytics will be highly preferred.

Mandatory SkillsOperations Research & Optimization

Strong academic and practical background in Operations Research (OR) and optimization. Hands-on experience with:

Mixed Integer Linear Programming (MILP) Mixed Integer Programming (MIP) Constrained Programming Non-Linear Programming (NLP) Mathematical optimization and simulation techniques Experience using optimization tools such as:

Gurobi CPLEX OR-Tools Pyomo Other optimization frameworks Trade Promotion & Revenue Management (Preferred)

Experience in one or more of the following:

Trade Promotion Optimization (TPO) Revenue Growth Management (RGM) Pricing Optimization Promotion Effectiveness Demand Forecasting Assortment Optimization Supply Chain Optimization Programming & Data Engineering

Strong programming expertise in:

Python SQL R NoSQL Spark / PySpark Experience with cloud platforms:

AWS Azure GCP Python Libraries & Frameworks

Hands-on experience with:

NumPy Pandas Scikit-learn TensorFlow PyTorch BERT Scapy Scrapy Machine Learning & Statistics

Strong understanding of:

Statistical analysis and hypothesis testing Predictive modelling Feature engineering Model evaluation and optimization Experience with algorithms such as:

Linear Regression Logistic Regression Decision Trees Random Forest SVM Neural Networks K-Means Clustering PCA Ensemble Models Key ResponsibilitiesData Science & Optimization

Design and develop machine learning, optimization, and analytics solutions for complex business problems. Build mathematical optimization models using MILP, constrained programming, and non-linear programming techniques. Develop scalable predictive and prescriptive analytics solutions. Solve business problems using data-driven insights and advanced algorithms. Client Engagement

Engage with clients to understand business challenges and convert them into analytical solutions. Present recommendations, business insights, and optimization outcomes to stakeholders. Build trusted advisor relationships with business and IT teams. Leadership & Project Management

Lead end-to-end AI and analytics engagements. Guide and mentor junior data scientists. Conduct project planning, effort estimation, and delivery management. Ensure project timelines, quality standards, and stakeholder expectations are met. Strategy & Innovation

Define analytics roadmaps and technology strategies. Support pre-sales and solutioning initiatives. Contribute to internal accelerators, products, and reusable assets. Preferred Domain Experience

Candidates with experience in any of the following domains will be preferred:

Retail Consumer Packaged Goods (CPG) FMCG Pharma Banking Insurance Supply Chain Revenue Management Key Search Keywords

Operations Research, OR, Optimization, MILP, MIP, Mixed Integer Programming, Constrained Programming, Non-Linear Programming, Mathematical Optimization, Gurobi, CPLEX, OR-Tools, Pyomo, Trade Promotion Optimization, TPO, Revenue Growth Management, RGM, Pricing Optimization, Demand Forecasting, Machine Learning, Python, SQL, Spark, TensorFlow, Statistics, Retail Analytics, Supply Chain Optimization. .