Scopely is looking for a

Product Analyst

to join our Star Trek Fleet Command team within our Live Games division in Barcelona, on a hybrid or remote basis.

We care deeply about what we do and want to inspire play every day - whether in our work environments alongside talented colleagues or through our deep connections with communities of players. We are a integral team of game lovers who develop, publish and innovate the mobile games industry, connecting millions of people around the world daily.

What You Will Do

Work across diverse teams through the full game development lifecycle, from defining goals and metrics through to monitoring and iterating actions based on live outcomes

Help set the direction for new features, analysing rich and varied data sets representing the behaviour of millions of players to understand where our best opportunities exist

Model and track data events, debugging and problem‑solving using your mathematical and systems thinking skills to ensure the quality and utility of your findings

Use SQL and Python to conduct deep‑dive analyses,



build robust analytical frameworks, and evaluate the impact of product launches, features, events, and live game changes on key player and business metrics

Apply advanced statistical modeling, causal inference, and machine learning techniques – such as Difference‑in‑Differences, regression modeling, predictive modeling, and other appropriate methods – to understand whether observed changes in metrics are driven by product actions, player behavior shifts, or external factors

Translate complex analytical results into clear, actionable insights for stakeholders across product, design, economy, engineering, and leadership teams

Present findings to multiple disciplines, using empathy, storytelling, and influencing skills to demonstrate relevance, support decision‑making, and raise the overall insight maturity of teams across the studio

What We’re Looking For

Successful track record in an Analyst, Data Sc

📌 Product Analyst (Madrid)
🏢 Scopely
📍 Madrid