I turn ambiguous product questions into clean experiments, SQL pipelines, and decisions backed by data.
- Product experimentation — A/B test design, sample-size planning, guardrail metrics, reproducible reporting. CUPED, AA-tests, Bonferroni, Bayesian A/B.
- User lifecycle analytics — cohort retention, funnel analysis, RFM segmentation.
- End-to-end delivery — SQL → Python → dashboard → stakeholder narrative.
📦 Portfolio: nikitaboyarkin.github.io
🔗 Links: linktr.ee/lofinibo
- 📚 Learning: Bayesian A/B testing, ClickHouse, dbt, Apache Airflow, Apache Superset
- 💼 Open to: BI / product / data analyst roles — actively interviewing
- 🤝 Collab: experimentation, user retention analytics
🐍 Snake classic · arrows / WASD
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🧪 A/B Test click until p-value < 0.05
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🏓 Pong beat the CPU · first to 11
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🔢 2048 merge tiles to 2048
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🔻 Funnel Drop catch users through the funnel
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Click a card to play the game full screen — opens in a new tab.
| Game | Controls | Goal |
|---|---|---|
| 🐍 Snake | Arrows / WASD · R to restart | Eat food, grow longer, don't hit walls or yourself |
| 🧪 A/B Test | Click anywhere (or Space / Enter) | Collect data until the p-value drops below 0.05 |
| 🏓 Pong | W/S or ↑/↓ — or move the mouse · R to restart | Beat the CPU — first to 11 |
| 🔢 2048 | Arrows / WASD · R to restart | Slide and merge tiles to reach 2048 |
| 🔻 Funnel Drop | ←/→ or move the mouse | Catch falling users to convert them |
End-to-end neobank analytics: funnel, A/B test, retention, segmentation. +6.24pp KYC conversion (Z=6.35, p<0.0001), €716K/yr — 48× ROI. CUPED + AA-test + Bonferroni.
Stack: Python · SciPy · Statsmodels · scikit-learn
Full-stack analytics platform: Streamlit + Supabase with RLS and an Edge Function. Significant A/B result (p=0.0034) on 8,000 seeded users, reproducible pipeline.
Stack: Python · Streamlit · Supabase · PostgreSQL
*Ten end-to-end SQL cases on DuckDB — funnel, N-day retention, DAU/MAU stickiness, LTV, streaks, A/B conversion, attribution. One command, no server, pytest-verified.*
Stack: SQL · DuckDB · pytest
- Дашборды, которые не врут: принципы продуктовой визуализации
- Кейс Volta: воронка, A/B и retention на синтетике необанка
- Воспроизводимые пайплайны данных: от seed до CI
- Оконные функции: gaps-and-islands, топ-N, range-join для stickiness
- Почему агрегат врёт: когортные треугольники удержания
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