Bhargav Shetgaonkar
Senior AI Scientist at Target
San Francisco, California, United States
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AI Scientist driving scalable retail modeling, causal inference, and supply chain decisions for Target's growing digital business. Previously, he was a Graduate student in AI at Duke with research stints at the Center for Advanced Hindsight and IBM Labs for their open source and pro bono initiatives.
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Building Retail Forecasting Agents using Copilot for Modern E-commerce Inventory Management
A practical exploration of hybrid AI-powered forecasting techniques tailored for e-commerce businesses facing unique supply chain challenges and demand patterns. This talk demonstrates how Copilot can be leveraged to build retail forecasting agents that account for distinct seasonalities, product cycles, and regional shopping behaviors. Learn how companies like Target and their vendor brands transform complex data into actionable inventory insights, balancing interpretability needed for effective cross-functional communication between data science and business teams in the modern marketplace powered by deep learning systems.
Session at Microsoft AI Conference
Traditional Time Series in Modern Times – A Retail Forecasters Guide
This talk explores the evolution of time-series forecasting in retail, highlighting how traditional statistical approaches can be integrated with modern neural network techniques to create models that are both precise and transparent. It discusses the challenges of using complex, black-box models in environments where understanding the influence of seasonality, holiday effects, and pricing dynamics is essential for collaborative decision-making.
Data Science Salon 2025
Deep Learning for Digital Demand Forecasting in Retail Categories
Retail demand forecasting has long been built on time series models that require historical sales for every SKU. In this talk, we trace the evolution of time series methods—from ARIMA and exponential smoothing to Transformer-based architectures—and introduce a hybrid zero-shot paradigm, where deep models leverage rich embeddings and meta-trained features to predict demand for entirely new products without/with minimal retraining.
Through a real-world retail case study, we demonstrate how zero-shot time series systems reduce stockouts and overstocks, scale effortlessly to thousands of SKUs, and open new avenues for explainable, real-time forecasting. Participants will leave with a clear roadmap for integrating zero-shot techniques into their forecasting pipelines that inform real-world short/long-term inventory decisions
Solo Talk at AI4 2025
Bhargav Shetgaonkar
Senior AI Scientist at Target
San Francisco, California, United States
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