Introduction
Atharv Yeolekar
Data Scientist / Machine Learning Engineer
Email:
Location:
Atlanta, GA
Organization:
Walmart Global Tech
A Bit About Me
Hi there! I'm Atharv Yeolekar, a Generative AI Data Scientist with a passion for developing cutting-edge machine learning models and big data solutions. I've had the pleasure of working at Walmart Global Tech, Grubhub, and as a researcher in a NASA and NSF-funded lab.
Through this blog, I aim to document my journey of learning and mastering Large Language Model concepts. I'll be sharing insights, discoveries, and practical applications in the form of a blog/journal. Join me as I explore the fascinating world of AI and break down complex topics into digestible and engaging content.
Let's embark on this learning adventure together!
Work Experience
September 2023 - Current
August 2022 - August 2023
August 2021 - July 2022
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Developed time series-based anomaly detection for brand performance metrics, identifying significant deviations.
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Led the development of a graph-based Product Association submodule, projected to generate $55M incremental profit.
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Scaled the Product Association submodule using PySpark and GCP, processing over 200 million items to enhance dynamic product bundling.
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Optimized supplier strategies through feedback from major suppliers like P&G, Unilever, and Nestle.
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Developed GrubLM (Grubhub’s Large Language Model) using BERT and RoBERTa, harnessing food data to generate refined embeddings for various downstream tasks.
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Created a pioneering algorithm leveraging Greedy KMeans and Boosting techniques to streamline CRM campaign targeting, enhancing profitability.
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Developed, deployed, and optimized a lifetime orders (LTO) prediction model on AWS using Jenkins and Azkaban, resulting in annual savings of $1.4M.
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Collaborated on a NASA and NSF-funded project, focusing on multivariate time series-based feature selection algorithms for space weather data.
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Implemented graph-based and class-separability feature selection methods to improve solar flare classification performance, contributing to advancements in space weather prediction models.
