Raghuveer Chanda
Senior Research Engineer at Google DeepMind
Mountain View, California, United States
Summary
👤
Senior🎓
Top SchoolRaghuveer Chanda is a Senior Research Engineer with 11 years of experience building end-to-end ML systems that power personalization and ad ranking across YouTube’s Player, Feed, Shorts, and multiple advertising verticals. He has driven millions in incremental advertiser value by shipping up-to-date user representations at scale, bridging research and production as a tech lead at Google and now at Google DeepMind. Trained at Carnegie Mellon in ML/AI for recommender systems and NLP, he combines rigorous academic foundations with practical deployment experience. Early work at Visa on transaction anomaly detection and contributions to open-source time-series tooling (implementing Holt-Winters models for spark-timeseries) reflect his strength in both statistical modeling and distributed systems. Based in Mountain View, he excels at translating complex research into revenue-driving product features across large, real-time platforms. Colleagues describe him as someone who consistently closes the loop from prototype to production while keeping models robust and interpretable.
11 years of coding experience
9 years of employment as a software developer
Master of Computational Data Science, Analytics, Master of Computational Data Science, Analytics at Carnegie Mellon UniversityBachelor's Degree, Computer Science, Bachelor's Degree, Computer Science at Indian Institute of Technology, Kharagpur
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (7)
scala10
apache-spark10
time-series10
data-analysis10
linear-algebra9
machine-learning8
testing8
Programming languages (3)
Github contributions (5)
sryza/spark-timeseries
Mar 2016 - Jun 2016
A library for time series analysis on Apache Spark
Role in this project:
Data Scientist
Data ScientistContributions:7 commits, 4 PRs, 9 comments in 3 months
Contributions summary:Raghuveer primarily focused on implementing and testing Holt-Winters exponential smoothing models for time series analysis within the Apache Spark environment. Their work involved modifying and adding test cases for the HoltWintersMultModel, ensuring the accuracy of its calculations. They also added initial level, trend, and seasonal parameter tests.
time-series-analysisapachesparkscalaapache-spark
amazonqa/amazonqa
Feb 2018 - Jun 2020
Evidence-based QA system for community question answering.
Contributions:173 commits, 2 PRs, 38 pushes in 2 years 4 months
nlpquestionpythonqa-systemmachine-learning
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