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Product Analyst

SQL · Python · A/B testing · retention

PhD in work psychology. Looking for a product analyst role — data and experiments.

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5 years in data

17 portfolio projects

+5.72 pp KYC conversion

€656K/yr annual fix impact

Business figures: portfolio case Volta (synthetic data) — not employment results.

Stack

Analysis SQL Python DuckDB

Experiments A/B Causal Inference

Infra GitHub Actions Supabase PostHog

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Experiments

Volta Neobank — Product Analytics

Fixed a neobank's onboarding bottleneck with an A/B test: +5.72pp KYC conversion, €656K/year. 23 projects: funnel → A/B → retention → segmentation → Market & Jobs → RAT v2 → causal.

Pythonpandas / NumPy +4

Impact: +5.72pp KYC conversion (Z=5.82, p<0.0001), €656K/yr (44× ROI), +9.2pp M3 retention, +€227K/yr incremental LTV, 4 data-driven user segments with per-segment monetization strategy, CUPED variance reduction + AA-test (type-I = 0.050) + Bonferroni correction, DiD check: the KYC fix causally lifted M3 retention by +9.09pp (95% CI [+6.21, +11.96]), RAT v2: 5 audit risks priced in money — 3 confirmed, 1 refuted by mechanism, 1 refined, 23 projects: 12 analytical domains + Market & Jobs (JTBD) + the RAT v2 validation layer + causal

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Live demo GitHub

Experiments

A/B Testing Methodology Toolkit

15 A/B methodology modules, each calibrated by simulation: an A/A null check holds Type I error at α, and power curves show the effect size each method can actually detect.

PythonNumPy / SciPy +2

Impact: 15 modules, each calibrated by simulation: Type I error ≈ α, power curves, CUPED drops variance by ρ², so the standard error by ~ρ, Naive peeking inflates Type I error — Pocock/OBF and mSPRT keep it under control, Delta method gives the correct SE for ratio metrics (CTR, RPC) — the naive per-unit t-test is biased, End-to-end pipeline: SRM → CUPED → delta-method CTR → per-segment ATE + BH → novelty check

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GitHub

Analytics

SQL Analytics Case Study

26 SQL cases: 25 on a synthetic ~183k-event dataset + 1 real-data case on UCI Online Retail II. Funnel, retention, LTV, attribution, anomalies, dbt layer on DuckDB, live report on GitHub Pages.

SQLdbt +4

Impact: 26 self-contained SQL cases (funnel → RFM) + 1 real-data case, dbt model layer on DuckDB (staging → marts, 17 dbt tests), Regression tests with deterministic invariants per case, Synthetic deterministic data (seed=42 + additive seed=43) + UCI Online Retail II, Live interactive report on GitHub Pages

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Live demo GitHub

Analytics

Cohort Analysis Dashboard

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.

Pythonpandas +3

Impact: Cohort retention matrix with triangular decay, ARPU / LTV by cohort with proper observation-age caveat, Tableau-ready export (CSV + .hyper extract), Reproducible seeded pipeline (seed=42)

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Live demo GitHub

Analytics

Python Analytics Playground

A module-based Python analytics toolkit — loading, cleaning, EDA, visualization — assembled into one pipeline with ≥80% pytest coverage. The base I copy into every new analysis.

Pythonpandas / NumPy +3

Impact: Modular load / clean / EDA / viz / pipeline modules with pytest coverage ≥80%, Single end-to-end pipeline runnable as `python -m python_analytics`, Requirements traced to a PRD (REQ-002 … REQ-006), tests tied to requirements, uv + ruff tooling, synthetic data for demo runs

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GitHub

Engineering

This Portfolio Site

The site you are reading: Astro 7, TypeScript and Markdown content collections, static output, dark/light theme, RSS, sitemap, JSON-LD and GitHub Pages deployment.

AstroTypeScript +2

Impact: Astro 7 + TypeScript + Markdown content collections, Dark/light theme with no-flash inline script, RSS, sitemap, robots, JSON-LD, OG/Twitter meta, Base-path-aware URLs for GitHub Pages hosting

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GitHub