Yash Kalyani

Yash Kalyani

Data Scientist, Walmart
Walmart

Bio

Yash is a Data Scientist at Walmart, where he has been working on Price Anomaly Detection for nearly two years. Yash has played a key role in enhancing Walmart’s pricing anomaly detection, driving initiatives that have led to impactful GMV growth. With a master’s degree in Statistics from University of Illinois Urbana-Champaign and a strong passionate for working with numbers, Yash is well-equipped to analyze and interpret complex data sets and help drive business decisions.

Optimizing Price Integrity: Walmart's Advanced Anomaly Detection System Using Quantile Regression, Neural Networks, and GenAI

Walmart’s Price Anomaly Detection system, currently deployed for both in-store and e-commerce items, uses advanced methods to identify pricing discrepancies. It moves beyond traditional classification techniques by leveraging Quantile Regression to define lower and upper price bounds, reducing the need for extensive labeling. The system focuses on predicting item prices, offering deeper insights into how various numerical inputs relate to actual prices. Neural Networks are employed to generate embeddings from product attribute features, predicting price bounds based on static categorical data. LightGBM enhances these predictions by incorporating historical prices, competitor data, and neural network outputs. To automate label predictions, Walmart uses GenAI, creating a feedback loop that improves model performance over time. This scalable, low-latency solution also ensures transparency through SHAP values, enabling model explainability.

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Data Scientist, WalmartWalmart

Optimizing Price Integrity: Walmart's Advanced Anomaly Detection System Using Quantile Regression, Neural Networks, and GenAI

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Data Scientist, Walmart
Walmart