Mohit Joshi
System Engineer at IBM
Uttarakhand, India
Summary
🤩
Rockstar🎓
Top SchoolMohit Joshi is a System Engineer based in Uttarakhand, India, with seven years of professional experience and a focused practice in Python and machine learning. Currently at IBM, he progressed from Associate System Engineer to System Engineer within a year, demonstrating rapid on-the-job growth and adaptability. He contributes to open-source machine learning tooling—adding test coverage for sparse matrix formats in the widely used scikit-learn project—bringing QA and test-automation rigor to core ML algorithms. Academically grounded with an MCA and a BCA, he blends formal training with hands-on engineering to bridge research-quality libraries and production systems. Colleagues describe him as curious and pragmatic—aptly captured by his GitHub tagline “I'm still figuring it out”—which drives continuous learning and incremental improvements. He’s particularly strong at hardening ML pipelines through robust testing and automation, an often-underappreciated but critical part of reliable model deployment.
7 years of coding experience
1 year of employment as a software developer
12th, PCM, 85.8 %, 12th, PCM, 85.8 % at GIC RamnagarBachelor of Computer Applications (BCA), Computer Science, 65%, Bachelor of Computer Applications (BCA), Computer Science, 65% at Kumaun University, NainitalMaster of Computer Applications - MCA, 8.34, Master of Computer Applications - MCA, 8.34 at Graphic Era Hill University
Github Skills (9)
scikit10
scikit-learn10
sparse-matrix10
python10
numpy10
testing10
data-science9
pytest9
machine-learning9
Programming languages (5)
Github contributions (5)
scikit-learn/scikit-learn
Aug 2023 - Nov 2023
scikit-learn: machine learning in Python
Role in this project:
QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:17 reviews, 12 PRs, 52 comments in 2 months
Contributions summary:Mohit's contributions primarily involve extending and enhancing existing tests within the scikit-learn repository. These commits focus on adding tests to cover a variety of sparse array formats (CSR, CSC, COO) within different modules such as SVM, decomposition, ensemble, and feature selection. The changes directly improve test coverage for sparse matrix support within scikit-learn's machine learning algorithms.
data-analysispythonstatisticsdata-sciencelearn-machine-learning
work-mohit/work-mohit
Jul 2020 - Feb 2025
Contributions:20 pushes, 1 branch in 4 years 7 months
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