Associate Lead Data Scientist - Operations Research

Job Description:

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 Skills: Operations Research & Optimization

Strong academic and practical background in Operations Research (OR) and optimization.

Hands-on experience with:

1. Mixed Integer Linear Programming (MILP)

2. Mixed Integer Programming (MIP)

3. Constrained Programming

4. Non-Linear Programming (NLP)

5. Mathematical optimization and simulation techniques

Experience using optimization tools such as:

1. Gurobi

2. CPLEX

3. OR-Tools

4. Pyomo

5. Other optimization frameworks

Trade Promotion & Revenue Management (Preferred)

Experience in one or more of the following:

1. Trade Promotion Optimization (TPO)

2. Revenue Growth Management (RGM)

3. Pricing Optimization

4. Promotion Effectiveness

5. Demand Forecasting

6. Assortment Optimization

7. Supply Chain Optimization

Programming & Data Engineering

Strong programming expertise in:

1. Python

2. SQL

3. R

4. NoSQL

5. Spark / PySpark

Experience with cloud platforms:

1. AWS

2. Azure

3. GCP

Python Libraries & Frameworks

Hands-on Experience With:

1. NumPy

2. Pandas

3. Scikit-learn

4. TensorFlow

5. PyTorch

6. BERT

7. Scapy

8. Scrapy

Machine Learning & Statistics

Strong Understanding Of:

1. Statistical analysis and hypothesis testing

2. Predictive modelling

3. Feature engineering

4. Model evaluation and optimization

Experience With Algorithms Such As:

1. Linear Regression

2. Logistic Regression

3. Decision Trees

4. Random Forest

5. SVM

6. Neural Networks

7. K-Means Clustering

8. PCA

9. Ensemble Models

Key Responsibilities: Data 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

Skills

Data Science, Analytics, Machine Learning, Operations Research, Data Analytics, Python, Statistics, Data Scientist, Data Modeling