Sumukh Ghodke
VP Data Science
Bengaluru, Karnataka, India
Summary
👤
Senior🎓
Top SchoolSumukh Ghodke is a VP of Data Science with nearly two decades of experience building production AI, ML and analytics systems across fintech, edtech, healthcare and enterprise software. He combines hands-on software engineering roots with senior leadership, having established unified ML platforms, MLOps practices and cross-functional data teams that drive retention, engagement and operational efficiency. A pragmatic modeler and experimenter, he translates business requirements into scalable data products and instrumentation for growth and forecasting. His early research and contributions to NLP tooling—evident in work migrating classification features into the NLTK ecosystem—underline a deep, research-informed approach to applied ML. He has founded and consulted startups, bringing product-focused analytics and KPI-driven growth frameworks to clients, and has led data engineering and simulation projects for hospitals and public health planning. Based in Bengaluru, Sumukh blends enterprise rigor with startup agility to deliver measurable business impact from data.
19 years of coding experience
13 years of employment as a software developer
B.E., Electronics and Communications, B.E., Electronics and Communications at R. V. College of Engineering, BangaloreThe University of Melbourne
English
Github Skills (7)
nltk10
machine-learning10
nlp10
python10
natural-language-processing10
ml10
unit-testing9
Programming languages (3)
Github contributions (5)
nltk/nltk
Mar 2007 - Apr 2009
NLTK Source
Role in this project:
Back-end Developer
Back-end DeveloperContributions:91 commits in 2 years 1 month
Contributions summary:Sumukh migrated basic classification functionality from a local repository to the NLTK contrib, indicating a focus on integrating or contributing to core NLP features within the project. Their work involved modifying files related to a confusion matrix and instance handling, suggesting an involvement in machine learning or statistical methods for evaluating NLP models. The user was involved in refactoring and adding unit tests related to the classification framework.
nlppythonmachine-learningnltknatural-language-processing
sparcs/dbt-date
May 2021 - May 2021
Date-related macros for dbt
Contributions:4 PRs, 3 pushes, 3 branches in 3 days
datawarehousemacrossqldbtelt
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