Product Manager / Analyst — Analytics & ML

About

Maestra.io is an all-in-one ecommerce marketing platform — a real-time customer data platform with email, SMS, push, on-site and product personalization, loyalty, referrals and product recommendations in one system, sold with a dedicated marketer attached to every account. Mid-market DTC brands use it to replace four or five separate tools; Urban Armor Gear, 4ocean, Enlightened Equipment, Selkirk Sport and Bokksu are among them.

US ARR grew five-fold in 2025 and the company is approaching $5M ARR. It is a small, founder-funded American business of around 40 people, built by a team with roughly twenty years in this domain, growing without venture funding and aiming at long-term profitability on the US market.

The role

Our client is looking for a hands-on data product manager to own the analytical side of the platform. The centre of the job is a semantic layer of metrics that both people and AI agents query — and around it, A/B testing methodology and the recommendation algorithms.

This is deliberately not a classical product manager role. It is hands-on: debugging reports, writing specs, working directly with engineering. Roughly 70% of the time is spent building and improving reports; the experimentation and ML work sits on top of that, not instead of it. There is no dedicated data science team, no analysts, no other PMs on this and no QA — there are very experienced colleagues who will tell you where to dig, Claude, and a design system. Everything else is yours.

You would report to the founder-CEO, who acts as CPO, with regular 1:1s, and another product manager will help you onboard.

What you own

• A BI system built on a semantic layer. Making the company's data available for analysis through MCP: describing and developing roughly 70 data marts covering attribution, loyalty, recommendations, site and email conversion, segments and subscribers. Designing new metrics, reports and dashboards — from prototypes in SQL and Metabase through to written specifications for engineering and acceptance of what they build.

• Experimentation across the platform. A/B tests for site personalization, campaigns, scenarios and control groups. Segment definitions, statistical significance, debugging. The goal is testing that is both understandable and reliable for people who are not statisticians.

• Recommendation algorithms. Matrix factorization and collaborative filtering today. Maintaining them, improving quality, introducing embeddings, and solving cold-start and sparse-data problems — including the fallbacks, the tuning, and making the behaviour explainable to the business.

How you’ll work

• AI-first. We expect heavy use of AI for SQL, Python, ML and, well, everything. AI writes — you verify. The statistics, the experimental design and the data model live in your head, because you are the last line of defense before a number reaches a customer.

• Deep questions over big teams. You’ll interrogate engineers, CSMs and merchants until the domain actually makes sense, and become the person everyone asks how a given number is computed.

• Tools. SQL over a multi-tenant lakehouse (Delta Lake / Trino / Spark, billions of rows), Python for analysis, Metabase for dashboard prototypes, Notion requirement cards, Slack threads with fast decisions and no bureaucracy.

• The team. Around 40 people in total; product and R&D is ten engineers, three product managers and the founder. You work horizontally with customer success and the other product managers rather than through layers — decisions happen in days, not quarters, and what you ship reaches real merchants within weeks.

Required

• Strong SQL, including a working understanding of the constraints of large datasets

• A mathematical foundation in A/B statistics — significance, sample sizing, and what makes a test trustworthy

• Experience automating and delivering features to production together with developers — writing the specification, working through it with engineering, and accepting the result

• Experience building dashboards, reports and metrics that other people depended on

• Direct experience working with end users or internal power users: asking questions, uncovering the real problem, turning it into a requirement

• A real appetite for the reporting work itself — the ML and experimentation sit on top of it, not instead of it

• Strong written communication, since most collaboration is asynchronous and in writing

• Fluent Russian, spoken and written — it is the working language inside the team

• English at B2 or above, with a willingness to keep improving it

• Based outside the United States, and able to work between EU and US Eastern hours

Preferred

• Ecommerce or martech domain knowledge

• Previous ownership of a semantic layer or metric layer that other teams built on

• Hands-on exposure to recommender or ranking systems

• Experience exposing data to AI agents or LLM tooling, for example through MCP

Hiring process

We run an asynchronous-first process, deliberately — it’s how the job works too.

1. Fill in the application form - attach your CV;

2. Have a Zoom interview with Hire5 Recruiter;

3. Intro conversation (30–60 minutes) with the founder-CEO / CPO.

4. Asynchronous Q&A — exchange questions and answers in a shared Google Doc.

5. Test task — asynchronous, and close to real work.

6. Final conversation on the results, together with another product manager.