---
title: "Devyanshi Singh - Data and Applied Scientist 2 in Bengaluru | Tal"
description: "Meet Devyanshi Singh, Data & Applied Scientist 2, Microsoft AI • ex-IBM • IITK’22 based in Bengaluru. AI stack includes claude code, claude, and codex. Explore public work links including github."
image: "https://tal.club/meet/devyanshi-singh/share-image"
---

# Devyanshi Singh on Tal

## Headline

Data & Applied Scientist 2, Microsoft AI • ex\-IBM • IITK’22

## About

I build the ML systems behind the ads you click \- and the ones you don't \(because relevance matters\)\. Over the past 4 years at Microsoft Ads, I've navigated the full advertising ML stack \- from triggering & selection to relevance, ranking, allocation, and pricing \- with deep hands\-on expertise in intent triggering, ad selection, and pClick prediction\. I build the systems that keep the ad platform's trifecta in harmony: user experience, advertiser conversions, and the publisher's revenue humming quietly in the background :\) Currently exploring: Agentic AI workflows, multi\-model orchestration, and how the next generation of AI tools reshapes the data scientist's craft\. Always happy to connect with people solving interesting problems\!

## Location

Bengaluru

## AI stack

claude code

claude

codex

github copilot

grok

hugging face

## Work experience

Data and Applied Scientist 2 at Microsoft, Mar 2025 \- Present · 1 yr 6 mos, Vertical Ads Team, Microsoft AI — Working on ad selection, ranking, and monetization systems for Microsoft's Vertical Ads platform\. \- Architected a unified offline evaluation pipeline for Filtered\-DiskANN ad selection, spanning query\-ad location relevance, precision, recall, hitrate, and multi\-intent analysis — used to calibrate the segment\-agnostic VA Selection model \- Oversaw quality analysis and production integration of SLM\-generated AdProfile attributes \(page summaries, SEO keywords, dynamic callouts\) for ad URL enrichment across the ad stack \- Drove end\-to\-end experimentation for AdProfile keywords in IRMatch & DiskANN Selection — from feature engineering and index flighting to defect tuning and prod code changes, delivering significant RPM uplift \- Pioneered LLM\-generated contextual keywords for Professional Services vertical selection, driving measurable CTR\-driven RPM gains \- Launched an image quality assessment pipeline for Hotel Ads landing pages, unlocking a new revenue stream from previously unservable ad copies

Data and Applied Scientist at Microsoft, Jul 2022 \- Mar 2025 · 2 yrs 9 mos, Vertical Ads Team, Microsoft AI — Owned the pClick prediction stack and LLM\-powered quality pipelines for Microsoft's global Vertical Ads platform\. \- Engineered an end\-to\-end global pClick prediction framework: training data pipeline, automated model refresh, online\-offline parity checks, and Treatment vs\. Control cross\-evaluation for any new feature \- Drove consistent monthly CTR, GoodClickYield, and offline AUC gains through automated model refresh cycles \- Iterated on the global pClick model incorporating SLM\-generated query\-profile signals, page position features, and seller bias corrections \- Built an LLM\-powered pipeline to detect and auto\-correct invalid advertiser feed entries, expanding ad coverage for Professional Service Ads \- Designed a GPT\-based Defect Labelling system for query intent, query\-ad relevance, and ad\-image relevance classification \(Hotel Ads\) \- Stood up two cold\-started verticals end\-to\-end — Doctors & Clinics and Lawyer & Legal Services — including multilingual intent triggering, allow/disallow lists, and pClick model\.

Data and Applied Scientist Intern at Microsoft, May 2021 \- Jul 2021 · 3 mos, Bengaluru, Karnataka, India, • Scrutinized Cross\-lingual embedding algorithms to transfer NER Ad\-Offer Tagger to low\-resource languages in a zero\-shot way • Modified MUSE & RCSLS algorithms on AEther, educed ‘pseudo parallel’ data using SCOPE, & visualized improved alignments • Obtained remarkable Precision up to 0\.96 & F1 Scores up to 0\.82 for FR & DE attributes, enhancing relevancy of Bing\-Ads Filters

Software Development Intern, Area631 at IBM, Jan 2021 \- Apr 2021 · 4 mos, Bengaluru, \- Optimized Apache Spark's workload scheduling using reinforcement learning \- Enhanced Spark's hashmap with Locality\-Sensitive Hashing \(HashAlign, Tree LSH, SimHash\) and deployed a DDQN\-based RL model for dynamic scheduling \- Achieved \~27% throughput improvement over baseline; recommended for production by IBM senior partners in collaboration with IBM Research, Zurich

## Education

Bachelor of Science \- BS, Mathematics and Scientific Computing, Indian Institute of Technology, Kanpur

Higher Secondary Education, Delhi Public School, Eldeco, Lucknow

## Public links

- github: https://github\.com/dev\-yan\-shi

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

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