Machine Learning Engineer – Pricing Optimizer Development
2 days ago
Location - Bengaluru (onsite)Notice Period - able to join within 15 to 20 daysBudget - OpenWe 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.
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