Daniel Ponikowski is a Senior Data Scientist and mathematician with 7 years of experience building end-to-end ML solutions across marketing mix modeling, churn and CLV prediction, recommender systems, and NLP. He blends rigorous academic training with hands-on production work at companies like Samsung and InPost, regularly applying both classic techniques (XGBoost) and modern deep-learning and gen-AI approaches. Daniel emphasizes translating business requirements into robust data models and carefully engineered training datasets, and he has a track record of proposing and delivering non-standard solutions and PoCs that push projects beyond typical approaches. His background includes research on anomaly detection, few-shot tabular learning, transfer learning, and LLM security, reflecting a curiosity for cutting-edge methods as well as practical impact. Based in Warsaw, he also brings experience building reusable tooling—such as an R package for seasonal sales modeling—and communicating results through stakeholder demos and trainings.
7 years of coding experience
5 years of employment as a software developer
Licencjat (Lic.) Matematyka, Licencjat (Lic.) Matematyka at Wrocław University of Science and Technology
studia podyplomowe Big Data - przetwarzanie i analiza dużych zbiorów danych, studia podyplomowe Big Data - przetwarzanie i analiza dużych zbiorów danych at Warsaw University of Technology
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