Ankish Bansal
Applied Scientist II Kindle at Amazon
Karnataka, India
Summary
👤
Senior🎓
Top SchoolAnkish Bansal is an Applied Scientist with 8 years of experience building AI-driven products at Amazon, currently enhancing the Kindle reading experience with production-ready content understanding and personalization models. His background spans speech recognition, ASR, and natural language understanding from roles across Alexa, AGI, and Kindle, grounded in an MTech in Machine Learning from IIT Kanpur. He balances research rigor with product focus—having moved from sequence-model experiments for end-to-end speech to scalable solutions used by millions of readers. Ankish is also an open-source-minded practitioner who has improved scikit-learn documentation to make complex ML algorithms more accessible. Based in Karnataka, India, he enjoys bringing together different AI subfields to craft intuitive user experiences and welcomes collaborations that bridge research and product impact.
8 years of coding experience
1 year of employment as a software developer
Indian Institute of Technology Kanpur
Stackoverflow
1,872reputation
184kreached
76answers
7questions
tensorflow
top-5%
keras
top-5%
Github Skills (14)
scikit-learn10
machine-learning10
python10
documentation10
scikit10
tensorflow9
keras9
data-science8
data-analysis7
statistics7
neural-network6
deep-learning6
tensor6
lstm6
Programming languages (2)
Github contributions (5)
scikit-learn/scikit-learn
Dec 2019 - Jan 2020
scikit-learn: machine learning in Python
Role in this project:
Technical Writer
Technical WriterContributions:5 commits, 7 PRs, 17 comments in 8 days
Contributions summary:Ankish's contributions primarily focused on improving documentation within the scikit-learn repository. The commits involve fixing default values and improving documentation for various machine-learning algorithms and classes, including Ridge regression, Logistic Regression, SGD, and Multilayer Perceptron. The edits demonstrate an effort to clarify parameter descriptions and provide better explanations for the library's functionality. The user's work contributes to making the library easier to understand and use for others.
data-analysispythonstatisticsdata-sciencelearn-machine-learning
ankishb/ml-toolbox
Apr 2019 - Mar 2020
Contributions:71 commits, 64 pushes, 2 branches in 10 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.