A/B Testing & Retention
Reproducible analytics: SQL, Python, CUPED and ship-gates. Every conclusion is re-checked in code, not taken on trust.
PhD in work psychology — I measure behaviour and causal effects, not correlations.
Featured case
Fixed a neobank's onboarding bottleneck with an A/B test: +6.24pp KYC conversion, €716K/year. A 17-project case on reproducible synthetic data.
Selected work
Each one proves a different part of the job — experimentation, analytics engineering, product metrics. Every claim has a reachable artifact.
01 Fixed a neobank's onboarding bottleneck with an A/B test: +6.24pp KYC conversion, €716K/year. A 17-project case on reproducible synthetic data.
02 25 end-to-end SQL case studies on a ~183k-event synthetic dataset: funnel, retention, LTV, attribution, anomalies. Runs on DuckDB with one command; live report on GitHub Pages.
03 A triangular cohort retention and LTV matrix on synthetic data: per-cohort ARPU and LTV with an observation-age caveat, plus a Tableau-ready CSV and Hyper export.
Who I am in facts — grade, experience, and measured outcomes.
Data Analyst / Product Analyst · Mid
Portfolio of reproducible analytics projects — experimentation, retention, segmentation — with full methodology and code. Built from project work, not from employment.
Tools and technologies
Explore the connections
All notes, projects, and topics are linked in an interactive graph. Find the path from SQL to A/B testing.
Open graph →Product analytics in writing
Methodology breakdowns, checklists, and templates for everyday analyst work.
Read notes →What's on the desk and what I keep as a working reference.
