Lead AI/ML Engineer

Role Overview We are seeking a highly skilled Lead AI/ML Engineer to design, build, and scale our next-generation Pricing & Promotion Optimization and Forecasting platform. This role sits at the intersection of machine learning, AI, and applied statistics, focusing on price elasticity modeling, personalized promotions, and demand forecasting across thousands of SKUs. You will collaborate closely with data engineers, product managers, and marketing analytics teams to create AI-driven decision systems that maximize business impact while ensuring scalability and technical excellence. The platform will support cross-channel regular price optimization across B2C and B2B segments, enabling consistent yet segment-aware pricing decisions that account for customer behavior, margin goals, competitive positioning, and channel-specific demand patterns.

Key

Responsibilities Design, Develop and deploy time-series forecasting models (eg., Prophet, ARIMA, DeepAR, LSTM, Temporal Fusion Transformer etc...) Apply statistical and causal inference techniques to evaluate promotion effectiveness and seasonality Build dynamic pricing, promotion optimization & recommendation models using reinforcement learning or multi armed bandits Architect ML pipelines for promotion targeting and ROI optimization Design end-to-end data workflows from data ingestion to model deployment, integration with production APIs and dashboards Develop cross-channel regular price optimization models across B2C and B2B, incorporating customer segment behavior, channel elasticity, contract constraints, competitive price signals, and margin guardrails Implement model monitoring, retraining, and drift detection systems Work with data warehouses (Azure SQL, Snowflake, Databricks Delta etc..), and APIs for data processing and operationalization Collaborate with cloud teams for deployment on Azure or GCP Core Business Problem Areas Pricing strategy and price elasticity modeling Cross-channel regular price optimization across B2C and B2B channels, balancing price consistency, segment-level elasticity, competitive positioning, and margin performance Promotion effectiveness, incrementality, and lift analysis Profitability optimization and revenue maximization Dynamic and rule-based pricing strategies Competition-based pricing Demand forecasting and price-demand curves Consumer behavior analysis related to promotions Seasonality-driven and peak-demand dynamic pricing

Requirements Basic

Qualifications Master’s degree in Computer Science, Data Science, Statistics, or related field 8+ years of experience in AI/ML solution design & development Strong proficiency in Python and ML libraries (eg., TensorFlow, PyTorch, Scikit-learn) Expertise in forecasting and time series modeling & analysis techniques Experience with feature engineering and model evaluation Familiarity with version control systems (eg., Git) and collaborative workflows Strong analytical and problem-solving skills Benefits