Machine Learning Engineer - Pricing Optimizer Development

Location - Bengaluru (onsite) Notice Period - able to join within 15 to 20 days Budget - Open We are looking for a skilled ML Engineer to design and build a robust, interpretable, and scalable optimization engine that enables data-driven pricing decisions.

The engine will leverage SKU-level demand, elasticity, and profitability data to recommend optimal pricing strategies based on business goals.

Key Responsibilities Refactor and enhance the existing optimization logic for speed, scalability, and modularity.

Model price-volume relationships using elasticity inputs at SKU and brand levels.

Develop optimization logic using constrained techniques (e.G., linear/quadratic programming).

Integrate business rules and constraints such as price bounds, SKU/brand-level caps, and TDP limits.

Enable scenario-based optimization with user-defined goals (e.G., maximize profit or volume).

Support multi-brand optimization without interdependency between SKUs and brands.

Collaborate with cross-functional teams to validate model behavior and outcomes.

Inputs You’ll Work With SKU-level data: volume, elasticity, profitability, TDP, and price bounds.

Business rules: brand-level caps, SKU exclusions, optimization targets.

Deliverables Modular Python-based optimization engine.

JSON-based input/output support for seamless integration.

Logging and fallback mechanisms for infeasible constraints.

Unit tests and validation with historical data.

Tech Stack & Skills Proficient in Python, especially NumPy, Pandas, SciPy or Pyomo/CVXOPT.

Experience with constrained optimization techniques.

(Preferred) Background in price optimization, demand modeling, or econometrics.