Key Responsibilities

Lead the design and development of optimization models (linear, non-linear, mixed-integer, stochastic, heuristic-based).
Translate complex business problems into mathematical and optimization frameworks.
Work closely with Business Analytics, Product, and Engineering teams to drive decision-making.
Develop end-to-end data science solutions: data exploration, feature engineering, modeling, validation, and deployment.
Guide and review work of junior data scientists and analysts.
Communicate model outcomes and trade-offs clearly to senior stakeholders.
Ensure scalability, performance, and robustness of models in production.

Mandatory Skills (Hard Requirements)

Strong hands-on experience in Optimization models (must-have):
Linear Programming (LP)
Mixed Integer Programming (MIP)
Constraint Optimization
Network / Scheduling / Resource Optimization
Proven experience applying optimization in real business scenarios, not just academic projects.
Advanced proficiency in Python (Pyomo, PuLP, OR-Tools, Gurobi, CPLEX, or similar).
Strong foundations in statistics, mathematics, and algorithms.
Experience working with large datasets and complex problem constraints.
Note: This role is not suitable for profile-heavy ML / dashboard-focused candidates without real optimization experience.

Good-to-Have Skills

Exposure to simulation techniques and scenario modeling.
Experience in industries like aviation, logistics, supply chain, pricing, operations research, or large-scale planning.
Cloud experience (AWS / Azure / GCP) for model deployment.
Experience leading teams or acting as a technical mentor.

Education Qualification

Mandatory:

Master’s degree or PhD in Data Science, Operations Research, Mathematics, Statistics, Computer Science, Engineering, or related field.
Strong academic grounding in optimization / operations research preferred.

Experience Expectations

Demonstrated ability to lead solution design, not just execute tasks.

Data Scientist