Senior Product Analyst (General Analytics)

ABOUT US

Scorewarrior is a game developer and publisher based in Limassol, Cyprus.

We are working on Total Battle — a cross-platform MMO 4X strategy game with a deep social system and extensive player interaction. We have 2 million monthly active players and continue to grow with the goal of becoming the #1 game in the genre.

Our team consists of 200+ passionate, talented, self-driven, and continually developing professionals who love making high-quality games.

ABOUT THE POSITION

We are looking for a Senior Product Analyst to join our General Analytics team: the analytics lead, an analytics engineer and you. Product teams have their own analysts; we take the research questions they cannot answer on their own, and we own how analytics is done across the company.

The core of the role is research: high-uncertainty product questions where the answer is the estimated effect of a specific factor, not a dashboard. Whether to extend the game's lifecycle, how to tell that a new giant feature works, how to run experiments on veteran players spread across hundreds of kingdoms. Alongside it you own the methodology: how experiments and effect estimates are done at Scorewarrior, and whether a number is good enough for a decision and useful to other analysts afterwards.

It is a senior individual-contributor role: close work with the analytics lead, short cycles with game designers and product managers. Pilots of the infra components (experimentation platform, visualization system, AI analyst assistant, ELT) are driven by the analytics lead and the infrastructure itself by the data tech lead; you join as the customer, not the builder. Game industry experience is not required.

WHAT YOU WILL BE DOING

•Take high-uncertainty questions from product teams (Old Players, New Players, Quality of Life, Platform) from a vague question to a decision the team acts on.

•Design and analyze experiments in non-standard setups: delayed treatment for veteran players, kingdom-level (cluster) tests, small samples, heavy-tailed revenue. Own variance reduction, outlier handling, permutation tests, MDE and power, reporting rules.

•Estimate effects where a classic A/B test is impossible, using causal inference and quasi-experimental designs, and turn one-off solutions into methods other analysts reuse: reference documents, dbt data marts, Tableau dashboards.

•Be the last check before the business acts on a number: the assumptions behind a metric, a test read-out or a pilot's output.

•Use Python inside research when SQL is not enough: a graph algorithm over clan and kingdom networks, an optimization problem, a simulation.

•Specify logging for features shipped by teams without their own analyst, and cover their ad-hoc questions.

REQUIRED QUALIFICATIONS

•Experience running research with high uncertainty end-to-end, from problem framing to a decision the team acted on. …

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