Radek Osmulski Portfolio
Senior Data Scientist (Recommender Systems) at NVIDIA and Kaggle Grandmaster, 1st-place iMaterialist Fashion Challenge winner; site documents fast.ai-rooted project work and a self-built learning tool (aiquizzes.com) alongside competition write-ups.
What makes it work
A breakdown of the choices that make this portfolio stand out.
Competition win in a named, verifiable challenge
A documented 1st-place finish at a named Kaggle competition is specific and checkable — far stronger evidence than a general computer-vision claim.
Built his own learning tool rather than only consuming others'
Creating aiquizzes.com shows initiative to productize his own learning process into something usable by others.
Applying community-course learning (fast.ai) into a production role
Tracing a path from taking a course to winning competitions to a senior recommender-systems role at NVIDIA is an honest, checkable trajectory.
What ML Engineers can take from this
Specific, actionable tips to apply to your own portfolio — no generic advice.
Document your learning path publicly (course → project → competition → job).
Build a small tool or app to teach something you learned.
Enter a named, verifiable competition win into your bio with the exact competition name.
Pick a production specialization rather than presenting as a generalist.
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