Tanuj Jain
Principal Machine Learning Engineer at DATANOMIQ
Berlin, Germany
Summary
🤩
Rockstar🎓
Top SchoolTanuj Jain is a Principal Machine Learning Engineer based in Berlin with a decade of experience building production-grade ML and recommendation systems across media and e-commerce. He progressed from research on group recommender systems to hands-on roles at idealo and Axel Springer, now leading ML efforts at DATANOMIQ where he focuses on applied GenAI and scalable model delivery. Tanuj contributes to open-source tooling for image deduplication—implementing evaluation metrics like average precision and NDCG—highlighting his emphasis on rigorous, reproducible model assessment. He combines an electrical engineering master’s background in signal processing with deep practical experience in retrieval, recommender and analytics pipelines, making him adept at bridging research and production. Colleagues describe him as a pragmatic engineer who brings measurement-first thinking to ambiguous ML problems.
10 years of coding experience
13 years of employment as a software developer
Master of Science (M.S.), Electrical Systems Engineering- Signals & Information Processing, Master of Science (M.S.), Electrical Systems Engineering- Signals & Information Processing at Paderborn UniversityBachelor of Technology - BTech, Electrical, Electronics and Communications Engineering, Bachelor of Technology - BTech, Electrical, Electronics and Communications Engineering at Guru Gobind Singh Indraprastha University
English, Hindi, German
Github Skills (2)
python10
test-automation10
Programming languages (4)
Github contributions (5)
idealo/imagededup
Apr 2019 - Jan 2023
😎 Finding duplicate images made easy!
Role in this project:
Data Scientist
Data ScientistContributions:4 releases, 16 reviews, 341 commits in 3 years 9 months
Contributions summary:Tanuj implemented core components for evaluating the performance of an image deduplication system. They developed code for calculating average precision, NDCG, and Jaccard similarity to measure the effectiveness of image retrieval. The user also added test scenarios and improved the logic of existing functions to ensure correct results for varying numbers of retrieved images and handle the case of zero image retrieval.
hashingpytorchpythonduplicatefinding
tanujjain/hello-world
May 2016 - Aug 2018
Contributions:2 PRs, 3 pushes, 3 branches in 2 years 3 months
react
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