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Product Analyst

Decisions from data, with a measurable effect

I ship decisions, not dashboards: which fix to switch on, whom to win back, where the hypothesis failed.

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

All projects →

Experiments

Volta Neobank — Product Analytics

Situation: «Volta» is a fictional neobank losing users during onboarding: every team had its own number, and none explained where the money leaked
Task: Find the critical onboarding step and prove it so the conclusion cannot be dismissed as a convenient sample
Action: One discover → validate → measure → optimize loop: funnel, KYC progress-bar A/B, cohorts, KMeans segmentation, DiD causality check
Result: KYC fix: +5.72pp conversion (p<0.0001) → €656K/yr at 44× ROI; M3 retention +9.2pp

Pythonpandas / NumPy +4

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Live demo GitHub

Experiments

Causal / Uplift — CUPED and Individual Treatment Effects

Situation: A two-sample t-test ignores the pre-period: experiments ask for more traffic than needed and retention offers go to everyone
Task: Implement CUPED and uplift modeling and prove neither is biased where the true answer is known in advance
Action: CUPED on a pre-period covariate and T-/S-learners on LightGBM; checked against ground truth with AUUC and QINI
Result: Standard error down ~26%; 5.6k users per arm instead of 10k; new users respond ~10x more than returning ones

PythonLightGBM +5

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GitHub

Analytics

Cohort Analysis Dashboard

Situation: Cohort retention and LTV on synthetic data: average retention hides the per-cohort dynamics
Task: Make retention and LTV readable per acquisition cohort rather than as a single average
Action: Retention matrix, ARPU and LTV by cohort, observation-age correction, Tableau export (CSV + Hyper)
Result: Cohort matrix instead of average retention: churn speed and where monetization diverges are visible

Pythonpandas +3

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Live demo GitHub

Analytics

SQL Analytics Case Study

Situation: No production data, and textbook exercises do not show systems thinking: 25 cases on a synthetic dataset
Task: Build cases where every query answers a product question and reproduces with one command
Action: One self-contained .sql per case, data generator on DuckDB, dbt staging → marts layer, regression tests, live report on Pages
Result: The funnel drops 54% at add-to-cart → checkout; retention falls from ~21% (D1) to ~5% (D30); only 3.5% repeat; on real data 72.4% return

SQLdbt +4

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Live demo GitHub

Analytics

Churn Prediction — Leakage-Free Retention Model

Situation: A churn model for a subscription product: a random split leaks future activity into training
Task: Score the model so its offline quality is not an artifact of leakage
Action: As-of features, a future inactivity label, a chronological split, decisions driven by recall@top-decile and lift
Result: In the top 10% riskiest the model catches 53% of real churners at 3.07x lift

PythonLightGBM +5

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GitHub

Analytics

RFM Analysis of Bank Clients

Situation: The bank accumulates client transactions, but marketing sends everyone the same offer
Task: Replace the single offer with targeted segments, each with its own retention strategy
Action: Cleaned the transactions, scored Recency, Frequency and Monetary, segmented clients, visualized the distribution in Tableau
Result: Four RFM groups: a small high-value share drives a disproportionate share of revenue; marketing moved to segmented scenarios

PythonSQL +1

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Live demo GitHub

Product

Product Analytics + A/B on Supabase

Situation: Analytics portfolios show metrics on a clean CSV; the hard part is analytics embedded in a real multi-tenant product
Task: Own the multi-tenant setup end to end: auth, per-org isolation, event ingest, and the experiment result computed in the database
Action: SQL views compute funnel, cohort, MRR, and DAU; v_results computes A/B in the DB; RLS isolates rows per organization
Result: A/B concluded: control 32.1% vs treatment 37.2%, +5.1pp at p = 0.0034 (χ²); metrics computed in the DB for any client

PythonStreamlit +3

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GitHub

Repo is private — available on request

Product

TaskFlow — PostHog Product Analytics Pipeline

Situation: An analytics portfolio usually starts from a ready-made CSV; this one starts earlier — with instrumenting the app
Task: Define events without PII leaks, get them into the analytics tool, and turn raw events into decisions
Action: Typed event catalog, PII scrubbing; A/B with chi-square, uplift, Wilson CI, SRM; SQL mirror, Streamlit dashboard, CI, Docker
Result: Full analytics lifecycle: 7 analyses, a typed event catalog with no PII leaks, metrics that reproduce in both Python and SQL

PythonPostHog +5

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GitHub

Repo is private — available on request

Engineering

This Portfolio Site

Situation: A static portfolio on GitHub Pages: projects and posts are edited in Markdown, not in component markup
Task: Make publishing a project a content change, not a code change: a new Markdown file, then a push
Action: Content collections with Zod schemas, withBase() for the base path, no-flash inline theme script, SEO set, push-to-Pages deploy
Result: Invalid frontmatter breaks the build, not the deploy; no link hardcodes the base; adding a project needs no code changes

AstroTypeScript +2

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GitHub