Adwait Baokar is a data scientist and software engineer with nine years of experience building production-ready models and backend systems for startups and large enterprises across healthcare and finance. He holds an MSc in Advanced Computing Science from King’s College London and has applied gradient-boosting models (XGBoost, LightGBM) to insurance underwriting and medical-claims prediction while also owning end-to-end tooling and monitoring for model performance. At Bank of America he automated middle-office workflows and built a failure-diagnosing chatbot, and his hands-on roles at Sattviko covered full-stack web work plus social-data analysis and sentiment tools to drive commerce. He contributes to open-source Python tooling, notably improving SymPy’s log-simplification logic and tests, evidencing attention to numerical correctness as well as production behavior. Comfortable switching between research-quality modeling and pragmatic engineering, he brings both algorithmic rigor and operational experience to data-driven product delivery.
9 years of coding experience
4 years of employment as a software developer
Bachelor of Engineering - BE, Information Technology, Bachelor of Engineering - BE, Information Technology at Medi-Caps Institute of Technology & Management Rau, Indore
MSc, Advanced Computing Science, MSc, Advanced Computing Science at King's College London
Contributions:94 commits, 26 PRs, 261 comments in 1 month
Contributions summary:Adwait primarily contributed to improving the `logcombine` function within the `sympy/simplify` module. Their work involved fixing logical errors and refining the mathematical transformations performed by this function to ensure accurate simplification of logarithmic expressions. Furthermore, the user updated the test suite (`test_simplify.py`) by adding new test cases to validate the correct behavior of `logcombine` and ensure the fixes are working as intended, which is critical to the correctness of the computer algebra system. They also made changes to the `exponential.py` file related to log expansion and rational number handling within logarithmic functions.
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