---
title: "Kaustabh Ganguly - Mentor - Artificial Intelligence / Data Science in Greater Bengaluru Area | Tal"
description: "Meet Kaustabh Ganguly, Senior AI/ML engineer @ Nielsen | M.Tech - AI @ IIT Madras based in Greater Bengaluru Area. AI stack includes antigravity, codex, and cursor. Explore public work links including github."
image: "https://tal.club/meet/kaustabh-ganguly/share-image"
---

# Kaustabh Ganguly on Tal

## Headline

Senior AI/ML engineer @ Nielsen \| M\.Tech \- AI @ IIT Madras

## About

Completed Master of Technology \(MTech\) in Artificial Intelligence at the prestigious Indian Institute of Technology, Madras, and currently contributing as a Senior Member of Technical Staff at Gracenote, a Nielsen company\. Core competencies include data engineering, machine learning, and MLOps practices, with a strong foundation in Python, Redshift, and AWS\. Academic and professional experiences are complemented by a Bachelor of Technology in Information Technology from Kalyani Government Engineering College\. At Gracenote, collaborates on cutting\-edge data\-driven solutions, leveraging expertise in data pipelines, machine learning modeling, and scalable architectures to deliver impactful outcomes\. Passionate about fostering innovation and driving efficiency, they bring a collaborative approach to solving complex challenges\. Dedicated to creating sustainable solutions that align with organizational growth and promote a culture of teamwork and progress\.

## Location

Greater Bengaluru Area

## AI stack

antigravity

codex

cursor

## Work experience

Mentor \- Artificial Intelligence / Data Science at IEEE SMC KGEC, May 2026 \- Present · 4 mos, Mentoring Students for IEEE SMC KGEC chapter\.

Senior Member of Technical Staff at Gracenote, Nielsen Company, Dec 2025 \- Present · 9 mos, Greater Bengaluru Area

Open Source Contributor at GitHub, Jan 2018 \- Present · 8 yrs 8 mos, Remote

Lead Data Scientist at Johnson & Johnson MedTech, Sep 2023 \- Dec 2025 · 2 yrs 4 mos, • Architected CCP data platform end\-to\-end \(ingestion → validation → feature pipelines\) with SOLID design and near\-zero rework\. • Built ODR \(On\-Demand Refresh\): one\-click ad\-hoc ETL → 0 orchestrator failures over 8\+ months; 0 refresh tickets; faster post\-fix re\-triggers\. • Built FPM \(Fail\-Proof Mechanism\): Excel→Parquet \+ dynamic Redshift DDL → incidents reduced from 15\+ to \~2 per month \(\~87%↓\); \~80% fewer support tickets\. • Shipped 4 production ML models across distinct business questions: Analyst\-action recommendation \(classification\) with SHAP waterfall explanations to analysts\. • Prototyped natural\-language Q&A over Tableau \(LangChain\) to speed self\-serve analytics\. • Established MLOps practices: Redshift feature stores, experiment tracking, approvals, post\-deploy monitoring, BA feedback loops; internal Flask APIs; CI/CD on Jenkins/Bitbucket\. Stack: Python, scikit\-learn/XGBoost, SHAP, SQL \(Redshift/Postgres\), Flask, Tableau, LangChain, AWS \(S3/EC2/RDS/Redshift\), Jenkins/Bitbucket, MLOps\.

Lead Data Engineer at Johnson & Johnson MedTech, Sep 2022 \- Aug 2023 · 1 yr, • Productionized “Data Sentry” for onboarding/offboarding \(DB access \+ AD groups\) across AWS/Azure/GCP on Lambda \+ S3 with OAuth/Microsoft Graph\. • Delivered 10k\+ lines of Redshift SQL in \~3 months → zero defects to production\. • Enhanced self\-service access for platform teams, significantly reducing operational toil and expediting SQL delivery\. Stack: Python/Pandas, AWS \(Lambda/S3\), OAuth/Microsoft Graph, Redshift SQL, Git/Jenkins\.

Data Engineer at Johnson & Johnson MedTech, Oct 2020 \- Aug 2022 · 1 yr 11 mos, • Ran production ETL on AWS \(Redshift/RDS/EC2/S3\) orchestrated with Control\-M; authored complex SQL for analytics and reporting\. • Built S3\-based cross\-database transfers and dirty\-data filters → eliminated delimiter\-related ETL failures\. • Tuned legacy Redshift views → \~3× faster runtimes and lower compute cost\. • Drove 50\+ production releases with <2% rework; SDET on 20\+ SIT cycles with 0% rework\. • Mentored peers on SQL patterns, release hygiene, and on\-call runbooks\. Stack: Python, SQL/Redshift, AWS \(S3/EC2/RDS\), Control\-M, Jenkins, Bitbucket/Git\.

Machine Learning Intern at Param\.ai, Jun 2018 \- Jul 2018 · 2 mos, Hyderabad Area, India, I played a key role in advancing Param\.ai's hiring automation efforts through the development of a sophisticated similarity matching algorithm\. • Leveraged advanced AI techniques to enhance candidate\-job fit, improving recruitment efficiency\. • Utilized Jaccard similarity and cosine distances on a custom\-trained Word2Vec model to optimize matching processes\. • Actively contributed to the startup's vision of transforming the hiring landscape for top candidates\.

## Education

Master of Technology \- MTech, Artificial Intelligence, Indian Institute of Technology, Madras

Bachelor of Technology \- BTech, Information Technology, Kalyani Government Engineering college

## Public links

- github: https://github\.com/stabgan

[View the original profile](https://tal.club/meet/kaustabh-ganguly)

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