Najeeb Kazmi
Senior Data Scientist
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolNajeeb Kazmi is a Senior Data Scientist at Microsoft with seven years of experience applying machine learning to product-facing problems, currently focused on building high-quality, engaging weather forecasting experiences. He previously contributed to the AI Platform’s Machine Learning Algorithms group, developing ML algorithms, experimentation frameworks, and performance benchmarking for internal teams. Najeeb’s open-source work includes performance testing the ML.NET PredictionEngine, helping ensure fast single-prediction latency across classic pipelines like iris, sentiment, and medical models. He holds dual master’s degrees in Computer Science and Statistics from Rutgers and a BS in Applied Mathematics from Yale, combining strong theoretical grounding with production ML engineering. Colleagues describe him as pragmatic and detail-oriented, with a knack for translating benchmarking insights into measurable product improvements.
7 years of coding experience
6 years of employment as a software developer
Master’s Degree, Computer Science, Master’s Degree, Computer Science at Rutgers University–New BrunswickBachelor’s Degree, Applied Mathematics, Bachelor’s Degree, Applied Mathematics at Yale University
Stackoverflow
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Github Skills (9)
net10
asp-net10
machine-learning10
benchmark10
benchmarking10
dotnet10
mlnet10
mldotnet10
algorithms9
Programming languages (6)
Github contributions (5)
dotnet/machinelearning
Sep 2018 - Apr 2020
ML.NET is an open source and cross-platform machine learning framework for .NET.
Role in this project:
ML Engineer
ML EngineerContributions:49 commits, 95 PRs, 40 pushes in 1 year 7 months
Contributions summary:Najeeb primarily contributed to the performance testing and benchmarking of the `PredictionEngine` within the ML.NET framework. Their work involved adding benchmark tests to measure the performance of single predictions for various machine learning pipelines, including those for iris classification, sentiment analysis, and breast cancer prediction. Furthermore, the user added prediction benchmarks using the legacy LearningPipeline API, showing a focus on testing both current and older implementations.
machine-learning-platformdotnetml-netmachine-learningcsharp
najeeb-kazmi/NimbusML
May 2019 - Feb 2020
Python machine learning package providing simple interoperability between ML.NET and scikit-learn components.
Contributions:2 PRs, 93 pushes, 37 branches in 8 months
pythonml-netdata-scienceinteroperabilitymachine-learning
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