Shoham Debnath
Lead Software Engineer at KKR
Gurugram, Haryana, India
Summary
👤
Senior🎓
Top SchoolShoham Debnath is a Lead Software Engineer based in Gurugram with 11 years of experience building data-driven systems for finance and risk. He has led model risk work at Goldman Sachs and now drives engineering at KKR, combining production software delivery with deep quantitative rigor. Shoham is an active pandas contributor, improving documentation and fixing regressions in one of Python’s most widely used data libraries—underscoring a practical focus on tooling that improves analyst productivity. His background spans credit and market data analytics, large-scale benchmark calibration, and quantitative research at firms like Deutsche Bank and WorldQuant, where he translated financial signals into robust models. Trained as a mining engineer at IIT Kharagpur, he brings an uncommon blend of engineering discipline and quantitative curiosity to production ML and risk systems. Colleagues rely on him for pragmatic solutions that bridge statistical nuance and scalable code.
11 years of coding experience
4 years of employment as a software developer
Bachelor's Degree Mining Engineering, Bachelor's Degree Mining Engineering at Indian Institute of Technology, Kharagpur
English, Hindi, Bengali
Stackoverflow
67reputation
12kreached
3answers
5questions
Github Skills (13)
pandas10
python10
data-science10
data-analysis10
documentation9
numpy8
java6
android-studio6
dynamic6
android-gradle-plugin6
android6
forloop6
decimal6
Programming languages (2)
Github contributions (5)
pandas-dev/pandas
Jul 2021 - Dec 2022
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Role in this project:
Data Scientist
Data ScientistContributions:157 reviews, 33 commits, 46 PRs in 1 year 5 months
Contributions summary:Shoham primarily contributed to documentation improvements and bug fixes within the pandas library, focusing on addressing issues related to data analysis and manipulation. Their work included correcting documentation styles, adding examples to documentation, and fixing regressions related to data type handling and function behavior, such as issues with `pd.cut`, `groupby.quantile`, and `to_datetime`. These contributions indicate a focus on improving the usability and reliability of the library's core functionalities.
pythondatalabeled-datamanipulationdataframes
debnathshoham/pandas
Jul 2021 - Aug 2024
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Contributions:461 pushes, 85 branches in 3 years 1 month
polarspythondatalabeled-datamanipulation
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