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