DATA SCIENTIST LEAD

Job Description

Role Purpose

We are seeking a highly experienced Data Scientist with deep expertise in Operations Research to join our advanced analytics team. In this role, you will lead the formulation and solution of complex optimization and analytical problems across domains such as Supply Chain, Retail, Telecom, and other industries. You will work on large-scale data-driven decision systems, combining optimization, simulation, and machine learning techniques to deliver measurable business impact. The ideal candidate will bring strong algorithmic thinking, hands-on modeling experience, and the ability to deploy scalable solutions.

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Experience Requirements:

10+ years of relevant experience in Data Science with strong focus on Operations Research

Proven experience across multiple domains:

Supply Chain (network design, inventory, logistics)

Retail (pricing, assortment, demand planning)

Telecom (network optimization, capacity planning)

Hands-on experience solving large-scale optimization problems in production environments

Experience with both exact solvers and heuristic approaches for complex problems

Demonstrated ability to translate business problems into mathematical formulations

Strong stakeholder communication and ability to explain complex models

Experience mentoring teams or leading OR initiatives

Key Responsibilities:

Lead end-to-end design and implementation of Operations Research and advanced analytics solutions across multiple domains.

Formulate real-world business problems into mathematical optimization models.

Develop and deploy large-scale optimization models for:
Supply chain network design, inventory optimization, routing, and scheduling.

Apply appropriate algorithmic techniques (exact, heuristic, stochastic) based on problem complexity and scale.

Perform simulation and scenario analysis to support decision-making under uncertainty.

Integrate forecasting models with optimization systems.

Collaborate with cross-functional stakeholders to translate business requirements into analytical solutions.

Drive production deployment using MLOps/Optimization Ops practices.

Mentor junior team members on OR methodologies and best practices.

Qualifications and Skills:

Core Data Science & Technical Skills:

Strong foundation in Data Science, Machine Learning, and Statistics

Advanced proficiency in Python (Pandas, NumPy, SciPy, Scikit-learn)

Experience with OR and optimization tools (Pyomo, PuLP, OR-Tools, CVXPY, Gurobi, CPLEX

Experience with data engineering (SQL, ETL) and cloud platforms is a plus

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Deliver

No.	Performance Parameter	Measure
1.	Demand generation	Order booking
2.	Revenue generation through delivery	Timeliness, customer success stories, customer use cases
3.	Capability Building & Team Management	% trained on new skills, Team attrition %

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Deep Algorithmic Knowledge (MANDATORY):

Optimization Techniques:

Linear Programming (LP): Simplex, Interior Point Methods

• Integer & Mixed Integer Programming (IP/MILP): Branch & Bound, Branch & Cut
• Nonlinear Programming (NLP) and MINLP
• Convex Optimization and Duality Theory

Network & Graph Algorithms:

• Shortest Path (Dijkstra, Bellman-Ford)
• Max Flow / Min Cost Flow
• Assignment and Transportation Problems

Dynamic & Stochastic Methods:

Simulation Techniques:

Heuristics & Metaheuristics:

Constraint-Based Methods:

Domain-Specific OR Models

Forecasting & Integration
Forecast + Optimization integration

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