About Me
Data Analyst / Product Analyst
Who I am
Profile
Nikita Boyarkin, data and product analyst. Focus — experiments, retention and segmentation: from hypothesis to metric, with code and SQL sitting next to every conclusion.
Background
I came to analytics from work psychology, which is why I start with “what exactly are we measuring” and only then run the numbers.
Experience & education
The short version: grade, experience, and measured outcomes.
Experience
Data Analyst / Product Analyst · Junior
Portfolio of reproducible analytics projects — experimentation, retention, segmentation — with full methodology and code. Built from project work, not from employment.
- A/B KYC: +5.72pp conversion → €208.5K in 12 mo
- Retention: +9.2pp → €227K LTV
- RFM: 4 segments concentrate 12% → 41% of revenue
- Telegram bot: weekly report 2h → 5 min
Education
- BSc · Business informatics · UMC 2016–2020
- MSc · Work psychology · UMC 2020–2022
- Doctoral programme · Work psychology · UMC 2022–2025
Now
Updated · October 11, 2026
- Building out this site: the personal layer, the knowledge graph, a 10-mini-game arcade.
- Maintaining an Obsidian knowledge base on product analytics — a structured Zettelkasten with automation.
- Preparing for BI / Data Analyst and Product Analyst junior-level interviews: SQL, pandas, algorithms, mocks.
- Experimenting with AI-assisted analytical workflows on Claude Code + MCP.
- Writing about practical data techniques: A/B testing, cohort analysis, RFM segmentation.
Reading: Designing Machine Learning Systems Chip Huyen.
Location / format
- Moscow, UTC+3.
- Remote or hybrid — by arrangement.
- Grade: junior — Data Analyst / Product Analyst.
- Full-time. Open to offers.
What I can help with
Offers plus proof. One contact — Telegram.
💼 Hiring
Data / Product Analyst, junior level. Flagship cases: Volta (neobank), A/B experiments, retention analysis. The methodology is reproducible: A/B and AA-tests, CUPED for variance reduction, the decision threshold fixed up front.
🤝 Collaboration
Cases, demos, the knowledge graph, the arcade — all of it is open: fork it, discuss it, reuse it. I write about practical data techniques: A/B, cohorts, RFM.
Cases with code, SQL and methodology live under Projects.
How to work with me
A relationship manual — so we don’t waste time figuring out the format.
Values
- Honesty with data — if the numbers contradict the hypothesis, we change the hypothesis, not the numbers.
- Speed of learning — try fast, learn fast, record the result.
- Direct feedback — no euphemisms, no “I feel like”.
Working style
- I like context before a request: why, for whom, what decision the result will drive.
- I ask clarifying questions — 5 questions up front beat rework at the end.
- I prefer metrics over impressions: “up 6pp” instead of “it got better”.
What matters to me in a team
- Data-driven decisions — metrics as the language, not decoration.
- Metric ownership — the team knows what it measures and why.
- An experimentation culture — hypotheses get tested, not debated.
Stack
Analysis
Visualization
Experiments
Retention & segmentation
Infra
GitHub · LinkedIn · Writing · Contact
GitHub activity
Source: GitHub API · updated 2026-11-02
- contributions this year
- 25,169
- public repos
- 54
- followers
- 437
- current streak (days)
- 96
- longest streak (days)
- 96
- on GitHub since
- 2019
Some of these are automated: daily data generation, site builds and checks, snapshot syncs. Code, analysis and writing are manual.
Activity over the last 30 days
Activity over the last 30 days
Daily contributions over the last 30 days — commits, pull requests, issues and reviews in public repositories.
- Over the last 30 days: 1465 contributions, averaging 48.8 per day.
- Peak activity: 160 contributions in a single day.
Contributions by month
Contributions by month
Total contributions per calendar month over the last 12 months.
- Over 12 months: 22038 contributions.
- The strongest month is Apr with 3873 contributions (17.6% of the annual total).
Contributions by week over the year
Contributions by week over the year
Total contributions per week (weeks start on Monday) over the last 12 months.
- Active weeks: 50 of 53, averaging 474.9 contributions per week.
- The most productive week: 1186 contributions.
Contribution types
Contribution types
How the year’s contributions break down by type: commits, pull requests, issues and code reviews.
- 25113 contributions in total over the year.
- The dominant type is Commits (25099, 99.9% of contributions).
Top languages
Top languages
Language share by code bytes across public non-fork repositories. Languages beyond the top 8 are grouped into Other.
- The primary language is Python at 37.4% of 10774688 bytes of code.
- The profile uses 14 languages in total; 9 are shown in the chart.
Ask me
Answers about my skills, projects and experience.
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