01Product leader · personalization & growth

Read the ground,
identify the figure.

PRODUCT LEADERSHIP|GROWTH-HACKING|UNIT ECONOMICS

all things value: creation → delivery → capture → scaling

AD·OS—1 self
GROUNDSIGNALOUT
CONCEPTDEEPTECH
open to projects & advisory

Personalization at scale. ML turned into real money.

Sber · Magnit OMNI. GMV, ARPPU, reach, retention.

20M+MAU led
10×vs control
11+products
20M
PERS individual offers by ML; 1,068 categories per customer
GROW one-week hypothesis pipeline; 20+ tests / 6 mo, 7 scaled
ECON per-customer P&L; investment cut, ARPPU held
ML×LAW recsys transformers; rules template adopted ecosystem-wide
subject documented · 4 functions · all load-bearing
Start a conversation
↑ flip MODE · concept states the claim, deeptech shows how it is built
02Experience

Three roles, three numbers.

Press a lens to highlight what is relevant, for example Growth & Economics, or just read.

Lens:
Head of Personalization
Magnit OMNI
2025 — now
↗ 2× feature MAU in six months through ML personalization
currentmobile · ecosystemMAU 20M
  • 10× business impact from ML look-alike audience selection: 2M RUB control vs. 30M test.
  • 2× feature MAU (4.7M → 10M) on cashback-categories personalization, 20 to 200 categories per customer.
  • One-week go-to-market for growth tests; installed the hypothesis launch and testing process.
  • 1,068 categories in the Q2 2026 upgrade, cutting investment with no ARPPU drop.
Personalization Projects Lead
Sber
2022 — 2025
↗ Personalization shipped across 11+ ecosystem products
unified B2C platform3.5 yrs
  • +5% GMV in Megamarket. The partner dropped their third-party SaaS after the A/B series.
  • 4.2h average time in-app from the "Power of Sound" scenario in Zvuk streaming.
  • 2.4× GMV from personalized selections at MRIYA Resort & SPA on SberTV.
  • Legal approval time halved via a recommendation-tech rules template, now used ecosystem-wide.
PMM
Antison
2019 — 2021
↗ Enterprise pilots with Unilever, Norilsk Nickel, LENTA
driver monitoringB2B SaaS
  • 5 enterprise pilots signed; partners Sinara Transport Machines and Renaissance Insurance attracted.
  • Sber500 Demo Day, pitched before Herman Gref and ecosystem leadership.
  • Best "Logistics" case, Rusbase Digital Awards 2021.
03Approach

The job isn't to build faster.
It's to decide what not to build, faster.

Structural analysis to find the 20% of moves that carry 80% of the impact, and a defensible no to the rest.

/01 judgment

Judgment over velocity

AI made prototypes cheap; judgment is the scarce asset. Opportunities framed in commercial terms, demand validated before building.

/02 tooling

Excel where it works, big guns where it counts

Most problems are discipline problems in tooling costumes. Heavy machinery only for hands-on AI builds and synthesis at scale.

/03 figure-ground

Figure from the background

Judge products by the behavior they install, not the features they list. Name what becomes scarce when capability makes something abundant.

/04 evidence

Product sense, backed by evidence

Probes, not opinions. Hunt the unnamed pain; run every idea through an adversarial loop ending in the cheapest disconfirming test.

▸ now a claude skill

Wrapped this methodology into a Claude skill. Try it yourself. It runs the four kill-gates on your own roadmap or build-vs-buy question.

04Highlights

Pet projects.

Things I build to keep the judgment sharp.

Claude skill

Judgment over Velocity

My methodology as an installable skill: four kill-gates that decide what not to build. Willing to answer "don't build anything."

Claude skill

Technical-Explanatory Minimalism

Turns Claude into a technical illustrator: exploded views, cutaways, explorable figures on faint graph paper. A figure becomes a model that yields many views.

macOS · monitor

Sentinet

See what your apps, and your AI agents, send to the net. Per-app traffic, one-click block, honest about what TLS hides.

05Background

Education

Faculty of Physics, NSUSemiconductor Physics, a statistical foundation

Methods

  • A/B testing & experiment design
  • Unit economics & KPI modeling
  • Evidence-based prioritization

Tools

  • Jira, Confluence, Notion
  • Python, scraping & analytics
  • draw.io, systems docs

Languages

  • Russian, native
  • English, C1
▸ how I can help

Hit DMs if you or your team wants to:

  • Set up a fast discovery and experimentation environment. Data turns into knowledge, insights, and actions.
  • Build an Amazon-style cross-functional team for a project across several domains, and succeed with it.
  • Estimate AI initiatives. Sort the fads from what to ship ASAP.