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