Alihan Zıhna
Staff Machine Learning Scientist at Fin
Dublin, Ireland
Summary
👤
Senior🎓
Top SchoolAlihan Zıhna is a Staff Machine Learning Scientist with 11 years of experience applying probabilistic modeling, time series, and deep learning to commercial problems across retail, consulting, and fintech. He has a proven track record of turning ambiguous business needs into production-ready solutions—ranging from automated stock replenishment and markdown optimization to high-accuracy sales forecasts and scalable churn models. Comfortable in Python, R, Spark, and cloud ML stacks, he has led data science teams and served as a consultant delivering projects across Europe, the Middle East, and the US. An active contributor to scikit-learn’s test suite, he brings careful engineering discipline to model quality and reproducibility. Based in Dublin, he pairs a master’s in Engineering and Technology Management with hands-on experience shipping ML in production and a knack for rapid online learning.
11 years of coding experience
12 years of employment as a software developer
Kenan Evren Anadolu LisesiMakine Mühendisi, Makina Mühendiliği, Makine Mühendisi, Makina Mühendiliği at Yıldız Teknik ÜniversitesiYüksek Lisans (Master), Engineering and Technology Management, Yüksek Lisans (Master), Engineering and Technology Management at Boğaziçi University
Turkish, İngilizce, German
Stackoverflow
95reputation
8kreached
9answers
0questions
Github Skills (16)
testing10
scikit10
pytest10
scikit-learn10
python9
machine-learning7
apply6
financial6
merge6
finance6
dplyr6
matrix6
ggplot6
dataframe6
r6
Programming languages (3)
Github contributions (5)
scikit-learn/scikit-learn
Feb 2021 - May 2021
scikit-learn: machine learning in Python
Role in this project:
QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:15 reviews, 19 commits, 21 PRs in 3 months
Contributions summary:Alihan primarily contributed to the test suite, modifying and updating existing tests using the pytest framework. Their commits focused on replacing older assertion methods with pytest's `raises` context manager, enhancing the testing style and efficiency. They addressed various test cases across different modules like discriminant analysis, multiclass classification, pipeline, naive bayes and mixture, and others, demonstrating a commitment to improving the robustness of the testing framework.
machine-learningpythonscikit-learnstatisticsdata-science
azihna/scikit-learn
Feb 2021 - Jun 2021
scikit-learn: machine learning in Python
Contributions:68 pushes, 24 branches in 3 months
pythondata-sciencelearn-machine-learningmachine-learningscikit-learn
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