Chennai, Tamil Nadu, India
Actively hiring, first seen by us 5 days agoWork setup in the employer’s posting: On-siteFrom the employer’s posting
Employment type in the employer’s posting: PermanentFrom the employer’s posting
Design and deploy time-series forecasting models for demand across thousands of SKUs
Build dynamic pricing and promotion optimization models using reinforcement learning or bandits
Evaluate promotion effectiveness and seasonality with causal inference
Develop cross-channel regular price optimization models for B2C and B2B
Implement model monitoring, retraining, and drift detection systems
Design, Develop and deploy time-series forecasting models
Build dynamic pricing, promotion optimization & recommendation models using reinforcement learning or multi armed bandits
Apply statistical and causal inference techniques to evaluate promotion effectiveness and seasonality
Develop cross-channel regular price optimization models across B2C and B2B
Implement model monitoring, retraining, and drift detection systems
Actively hiringFirst seen by us 5 days ago, still listed, and not a repost of an earlier posting.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Day 5
Early. Similar jobs usually come down between day 6 and day 70.
At Staples Trust, jobs usually come down by about day 81.
From 4,444 similar postings that came down, and 5 of Staples Trust’s. Coming down is not always a hire.
11 open jobs
Open jobs by the week we first saw them, last 12 weeks.
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Role Overview: We are seeking a highly skilled Senior 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 ops/ 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 11+ 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
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New ML Engineer postings at the employers we follow are up 12% over the last 5 weeks compared with the 5 before.
Each bar is one week of new postings. The darkest bars are the last 5 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
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Chennai, Tamil Nadu, India
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