Data Analyst with 3+ years of experience focused on fraud and behavioural analytics. Currently at Wildberries (Russia's largest online retailer, 200M+ MAU), evaluating fraud and anti-bot scoring models, designing behavioural alerts, and applying graph methods to investigate user connections.
🔍 Currently: open to UK fintech / fraud-analytics roles
📍 Location: London, UK (work-eligible, no sponsorship needed)
💼 Reach me: LinkedIn
| Project | What it shows |
|---|---|
| network-cluster-analysis | Graph methods (centrality, community detection) — transferable to fraud network analysis |
| digital-focus | Full-stack web app with custom event analytics pipeline (ClickHouse + serverless API) |
| mobile-listing-ab-test | A/B test analysis with proper statistical testing by metric distribution |
| financial-behavior-tech-professionals | Independent research: survey design → analysis → publishing |
Languages: Python, SQL
Data: pandas, NumPy, scikit-learn, NetworkX, igraph, statsmodels, scipy
Databases: ClickHouse, PostgreSQL, MySQL
BI: Superset, Redash, Power BI, Yandex DataLens
Pipelines: Airflow