Product Analyst

Experiments

A/B Testing Methodology Toolkit

Situation: An A/B method is only as good as its Type I error under the null and its power under a real effect
Task: Make the method guarantees verifiable, backed by a simulation run rather than by a citation
Action: Modules from the primary literature — SRM, CUPED, delta method, mSPRT, HTE, BH — each calibrated by simulation
Result: Type I error ≈ α for every method under its null; CI coverage ≈ 95% for the bootstrap; 15 modules calibrated by simulation

PythonNumPy / SciPy +2

Experiments

Browser Mini-Games — Analytics Arcade

Situation: Analytics concepts are abstract: a recruiter or student can't feel them from text
Task: Make a metric's meaning obvious from the action itself, not from an explanation
Action: Self-contained SVG games: A/B test, funnel drop, cohort catch, retention day, SQL and metric match
Result: 10 games: 7 analytics and 3 arcade, one file with no server; Playwright smoke tests catch load failures

SVGJavaScript +2

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

Analytics

Sales Calls Analytics Dashboard

Situation: An AI call flow is a 4-step funnel over 16,891 synthetic calls; the export does not say which step loses the client
Task: Take call breakdown off manual review and name the funnel step that loses the most clients
Action: Step labels from templated script markers, leak broken down by type, weighted contact loss, engagement by hour
Result: An answer in a minute: which step loses the client and what to fix; the funnel yields weighted contact loss and a first A/B scenario

PythonStreamlit +1

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

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

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

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

Analytics

Python Analytics Playground

Situation: In product analytics most tasks start the same way: load an export, clean it, check distributions, chart it — from scratch each time
Task: Turn the routine into shared modules that teammates can extend without fear of breaking existing behavior
Action: load / clean / EDA / viz / pipeline modules mapped to PRD requirements; tests in tests/ check those requirements; uv + ruff tooling
Result: pytest coverage ≥80%; modules stay small and single-purpose, requirements live in the PRD, and the tool extends without breakage

Pythonpandas / NumPy +3

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

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

Repo is private — available on request

Product

Product Analytics Dashboard (Streamlit)

Situation: The portfolio shows the whole AARRR cycle on one dataset of 8,000 synthetic users, not one metric in isolation
Task: Build the metrics so they read as one coherent story rather than a set of disconnected charts
Action: Deterministic seed and shared cache: one base for Funnel, Retention, Revenue, and Segments; 5 Streamlit pages
Result: Funnel, Retention, Revenue, and Segments read from one consistent base; the same UI became the Supabase project's presentation layer

PythonStreamlit +1

Repo is private — available on request

Engineering

Reporting Automation Telegram Bot

Situation: Every week an analyst hand-assembled metrics from several sources, refreshed a dashboard and posted screenshots to the chat
Task: Take the weekly report off one person so it arrives on time with no manual steps
Action: SQL against the data mart, KPI aggregation, a report template with a table and sparklines, Telegram Bot API delivery, fallbacks
Result: Manual 1–2h of prep became a cron job; the report arrives on schedule and the analyst interprets instead of copying numbers

Pythonaiogram / telegram-bot +3

Live demo

Repo is private — available on request

Engineering

Scrolly English Speaking

Situation: Workplace English (A2–B1) stalls for many Russian-speaking specialists: the grammar is there, coherent speech is not
Task: Close the gap between grammar and live speech: give learners situations where phrases are used, not recognized
Action: Single ScrollyLayout, MDX narrative sections, typed data module for viz props, IntersectionObserver with lazy imports
Result: Narrative and visualization in sync: the chart changes as the reader reaches a paragraph; a new scene is just a data file plus MDX

AstroTypeScript +3

Engineering

Digital Garden

Situation: Chronological posts age and lose connections; a digital garden links notes with wikilinks instead
Task: Publish a personal Zettelkasten as a static site where the links between notes are visible to the reader
Action: Atomic notes in Obsidian-flavored Markdown; Quartz: TypeScript plugins, backlinks, graph view, static output
Result: Links over chronology: backlinks make each note a node, and the graph turns note accumulation into a navigable structure

TypeScriptQuartz v4 +2

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