Kailash Budhathoki
Senior Applied Scientist at Amazon
Tübingen, Baden-Württemberg, Germany
Summary
🤩
Rockstar🎓
Top SchoolKailash Budhathoki is a Senior Applied Scientist with 15 years of experience who leads a science team at AWS Deep Science optimizing foundation models for inference in Amazon Bedrock. He has a strong causal-inference research background (PhD) and a track record of translating academic methods into high-impact production systems at Amazon, including multi-million dollar Ads solutions and supply-chain tools that dramatically cut investigation time. An active open-source contributor, he improved DoWhy with new modules, documentation, and an API for average causal effect estimation, bridging research and practitioner tooling. Based in Tübingen, he combines deep theoretical expertise in discrete causal inference with pragmatic engineering—often focusing on explainability and root-cause methods that scale across organizations.
14 years of coding experience
10 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Max Planck Institute for InformaticsMaster's degree, Computer Science, 1.1 (best 1.0), Honor's Degree, Master's degree, Computer Science, 1.1 (best 1.0), Honor's Degree at Universität des SaarlandesBachelor's degree, Computer Engineering, 79.6%, Bachelor's degree, Computer Engineering, 79.6% at Institute of Engineering, Pulchowk Campus
English, German
Github Skills (9)
causal10
causality10
python10
causal-inference10
documentation10
graphical-models9
machine-learning9
github8
bayesian-network6
Programming languages (2)
Github contributions (5)
py-why/dowhy
May 2022 - Nov 2022
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
Role in this project:
Back-end Developer & Technical Writer
Back-end Developer & Technical WriterContributions:75 reviews, 13 commits, 10 PRs in 6 months
Contributions summary:Kailash contributed to the DoWhy library by adding comments to clarify the implementation of the 3-step counterfactual algorithm. They fixed a minor typo in the added comments. Furthermore, they added a new module and implemented user guides related to quantifying arrow strength and intrinsic causal influence, thus providing helpful documentation and explanations of the library's functionalities. Finally, the user contributed to a new API for estimating average causal effect using GCM, including both the API itself and corresponding tests.
fairness-mlcausal-modelspythoncausalbayesian-networks
kailashbuki/predator
Sep 2011 - Feb 2022
Contributions:66 commits, 1 push in 10 years 6 months
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.