Summary
Dinar Mingaliev is an experienced Data Scientist based in Vienna with eight years of delivering data-driven solutions across banking and telecom. He has a strong track record of productionising models and pipelines—raising a telco churn ROC‑AUC from 68% to 78%, automating KPI and anomaly-detection workflows over terabytes of Hive data, and maintaining model monitoring and deployment processes. Comfortable across R, Python, Hive, Spark and tooling like Tableau and Bamboo, he blends classical econometrics and credit-risk modeling with modern ML (XGBoost, deep learning) and is actively transitioning into computer vision with PyTorch. His background in applied mathematics and econometrics, plus hands-on DevOps ownership in production, gives him a rare combination of statistical rigor and operational reliability. Not obvious from titles: he repeatedly improves systems end-to-end—from feature engineering and target balancing to memory tuning and YAML-driven automation—to achieve robust, repeatable results.
8 years of coding experience
6 years of employment as a software developer
Nanodegree, Computer Vision, Nanodegree, Computer Vision at Udacity
Master of Science (MS), Management and Applied Mathematics, Master of Science (MS), Management and Applied Mathematics at Moscow Institute of Physics and Technology (State University) (MIPT)
Master’s Degree, Econometrics, Banking, Corporate, Finance, and Derivatives, Master’s Degree, Econometrics, Banking, Corporate, Finance, and Derivatives at New Economic School
High school diploma, Physics and mathematics, Magna Cum Laude, High school diploma, Physics and mathematics, Magna Cum Laude at Physics and mathematics lyceum №3 (Cheboksary, Russia)
English, Spanish, Russian