{"basics":{"name":"Vladislav Morozov, PhD","label":"Data Scientist ","picture":"assets/img/logo.png","email":"vladislav.v.morozov [at] gmail.com","website":"https://vladislav-morozov.github.io/","summary":"Experienced data scientist with research-level expertise in causal inference and machine learning. Expert in extracting insights from large observational and survey data.\n","location":{"city":"Barcelona","countryCode":"ES"},"profiles":[{"network":"LinkedIn","username":"Vladislav Morozov","url":"https://www.linkedin.com/in/vladislavvmorozov/\""},{"network":"Github","username":"vladislav-morozov","url":"https://github.com/vladislav-morozov"}]},"work":[{"company":"Glovo","position":"Senior Data Scientist","startDate":"2026-03-01"},{"company":"University of Bonn (Bonn, Germany)","position":"Assistant Professor of Statistics (tenure track)","startDate":"2024-09-01","endDate":"2026-02-28","highlights":["Developed and validated causal inference methods for complex observational data (e.g. 2 top field publications, with Python package and MATLAB implementations)","Led modernization of data science instruction and launched 3 new courses (causal inference with unobserved heterogeneity; econometrics; simulations in data science) to 90%+ positive evaluations","Supervised and mentored 15 BSc/MSc students in causal inference and ML, guiding projects and code reviews"]},{"company":"Universitat Pompeu Fabra (Barcelona, Spain)","position":"Research Technician","startDate":"2023-11-08","endDate":"2024-08-31","highlights":["Prototyped scalable PySpark/Docker pipelines for novel high-dimensional time series analysis, demonstrating feasibility for large-scale deployment and value of further research ","Contributed to 'Modern Challenges in High-Dimensional Data Analysis' and 'New Frontiers in Econometrics' grants."]},{"company":"Universitat Pompeu Fabra (Barcelona, Spain)","position":"Doctoral Researcher and Instructor","startDate":"2018-10-01","endDate":"2023-11-07","highlights":["Led own research on causal inference for heterogeneous panel data, designing estimators, validating via simulation and large micro datasets, published dissertation 'Essays in Heterogeneous Panel Data Econometrics'","Taught 40+ sections of data science, econometrics, and machine learning courses (undergraduate to professional), with 4.5+/5 evaluations","Designed practical labs and exercise sessions: wrote theoretical exercises and created Python/R/Stata materials for econometrics, forecasting (undergraduate/PhD) and NLP for Economics (BSE Data Science Summer School)"]},{"company":"Federal Antimonopoly Service (Moscow, Russia)","position":"Junior Antitrust Data Analyst","startDate":"2016-05-01","endDate":"2016-10-31","highlights":["Developed the statistics and implementation for a system to triage consumer complaints about price collusion. By analyzing scanner data, the system increased the share of complaints receiving review from 60% to 90%","Built and automated dashboards to track competitiveness across 15 key food groups, establishing the authority's first real-time data pipeline from a fragmented data lake"]}],"education":[{"institution":"Universit Pompeu Fabra","area":"Econometrics","studyType":"PhD","startDate":"2019-10-01","endDate":"2024-05-01","gpa":"Excelente Cum Laude","courses":["Thesis: 'Essays in Heterogeneous Panel Data Econometrics'"]},{"institution":"Universit Pompeu Fabra","area":"Economics, Finance, and Business","studyType":"Master of Research","startDate":"2018-10-01","endDate":"2019-08-01","gpa":"9.3/10","courses":["Chosen path: Data Science"]},{"institution":"Barcelona School of Economics","area":"Economics","studyType":"Master of Science","startDate":"2017-09-01","endDate":"2018-08-01","gpa":"9.1/10"},{"institution":"Moscow State University","area":"Economics","studyType":"Bachelor","startDate":"2013-09-01","endDate":"2017-07-01","gpa":"5.0/5.0"}],"publications":[{"name":"Inference on Extreme Quantiles of Unobserved Individual Heterogeneity ","publisher":"Econometric Theory","releaseDate":"2026-01-01","website":"https://www.cambridge.org/core/journals/econometric-theory/article/abs/inference-on-extreme-quantiles-of-unobserved-individual-heterogeneity/AAA04119262FE0C273C63C6B456EDE19","summary":"Methods for inference on tails of distributions when only noisy data is available"},{"name":"Unit Averaging for Heterogeneous Panels","publisher":"Journal of Business and Economic Statistics","releaseDate":"2025-11-11","website":"https://www.tandfonline.com/doi/full/10.1080/07350015.2025.2584579","summary":"Efficient ensemble prediction/estimation of individual forecasts/parameters  in heterogeneous data settings"}],"skills":[{"name":"Programming","keywords":["Python","MATLAB","R","Shell scripting","SQL"]},{"name":"Classic and Deep Machine Learning, Causal Inference","keywords":["scikit-learn","PyTorch","transformers","EconML/DoubleML","statsmodels"]},{"name":"Data and DevOps","keywords":["Git","Docker","CI/CD","Testing (unit, integration)","Monitoring","Dashboards","Great Expectations","Spark"]}],"languages":[{"language":"English","fluency":"Native speaker"},{"language":"Spanish","fluency":"Native Speaker"},{"language":"Russian","fluency":"Native Speaker"},{"language":"German","fluency":"Limited working proficiency"},{"language":"Catalan","fluency":"Elementary proficiency"}]}