---
title: "Nitesh Kansal - Staff Engineer in Bengaluru | Tal"
description: "Meet Nitesh Kansal, ML & Engineering@Google | ex- Amazon | ex-Microsoft based in Bengaluru. AI stack includes claude code, codex, and cursor."
image: "https://tal.club/meet/nitesh-kansal/share-image"
---

# Nitesh Kansal on Tal

## Headline

ML & Engineering@Google \| ex\- Amazon \| ex\-Microsoft

## Location

Bengaluru

## AI stack

claude code

codex

cursor

deepseek

hugging face

langchain

## Work experience

Staff Engineer at Google, Jun 2025 \- Present · 1 yr 4 mos

Principal Applied Scientist at Microsoft Bing Ads, Apr 2024 \- Jun 2025 · 1 yr 3 mos, Bengaluru, Karnataka, India

Senior Applied Scientist at Amazon, Feb 2020 \- Dec 2022 · 2 yrs 11 mos, Bengaluru, Product Trust Score \(PTS\) to enhance Selection and Ranking in Amazon Choice, Recommendation and Search\. • Launched a Unified Global Deep Learning \(DL\) model in 16 countries world\-wide; measured annualized business impact of \~50 MM $ in an online A/B test, after integration in just 4 out of 16 countries\. • Designed a principled and data driven approach for identifying “what to share?” in Multi\-Task Learning setting using Fisher Information in weight parameters\. • Designed a retraining pipeline based on Continual Learning \(CL\) approaches to adapt to local country level changes while maintaining performance on other countries\. • Mentored a team of scientists and interns to improve on Cold Start products using Graph Convolution Networks and advanced dropout strategies and design a novel scalable Uncertainty Estimation method based on quantile regression\. • Responsible for yearly planning for PTS; includes identification of problem statements for offline experimentation and research\. • Received accolades from team members for effective guidance and skill sharing, and from manager for general effective management of the project and global launch\.

Applied Scientist at Amazon, Feb 2018 \- Feb 2020 · 2 yrs 1 mo, Bengaluru, Karnataka, India, Influencing Bidding decisions via\. user engagement prediction • Leveraged recent user engagement across webpages to infer future engagement on a specific webpage\. User engagement probabilities were used for improving real time bidding decisions\. • Improved engagement models on cold URLs by leveraging URL hierarchies and implementing hourly aggregation pipelines to get more recent URL level engagement information\. • Explored Semi\-Supervised methods to handle partially labelled data and latent variable survival methods to predict duration of stay and hence improve viewability models\. • Provided guidance to scientists and engineers in the team for building models and pipelines to predict various user engagement signals\. • Collaborated cross\-functionally to integrate user engagement metrics in Amazon Ad offerings\. • Various model improvements lead to 10% and 33% increase in ‘High Engagement’ ad inventory for display and video ads respectively\. Detecting robotic or malicious activity on Amazon Ads • Collaborated with other scientists in designing a Collusion Detection algorithm by applying Tensor Factorization and Sequence modelling approaches on customer Ad interaction data\. • Improved label quality using weak supervision, label propagation, etc, for a back bone supervised classification model acting at the ad click level\. Enabled use of high cardinality categorical features into the model which otherwise would have biased the model\. • Designed a counter\-factual based root causing algorithm which recursively breaks a macro anomaly into smaller anomalies and hence finds a small set of problematic entities\. • Collusion Detection and Counter\-Factual based Anomaly Detection algorithms together led to \~20MM $ of saving in bid value for the Advertisers annually\.

Senior Data Scientist at Media\.net, Jul 2017 \- Feb 2018 · 8 mos, Bangalore, • Productionized a CTR and CVR model for company’s DSP product; normalized for position bias; used URL hierarchy to deal with cold start URLs; explored Deep Factorization Machine to learn from very sparse data\. • Explored topic modelling and word embedding approaches to learn URL text representations used in CTR/CVR models of a SSP product\.

Senior Data Scientist at Dream11, Apr 2016 \- Jul 2017 · 1 yr 4 mos, Mumbai Area, India, • Created a new user recommendation engine leveraging user demographics; led to 50% increase in conversion and 20% increase in retention of new users\. • Developed Customer\-Match propensity model; and collaborated with marketing head to devise a deals targeting strategy based on the score; 2% increase in net entry fee collected\. • Designed a Collusion Detection algorithm to mitigate referral fraud; identified suspect clusters offline and blocking same cluster joins; saved INR 1M in cash bonus\. • Collaborated on SOP docs for setting possible fraudulent behaviours that manual annotators should look for in flagged accounts and hence generate labels for supervised model\. • Took sessions on various topics in Machine Learning for other scientists; Linear Algebra, Statistics and Probability, Machine Learning Basics, Graphical Models, etc\.

## Education

Dual Degree in Chemical Engineering, Chemical Engineering, Indian Institute of Technology, Delhi

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

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