We are on a mission to pioneer the world’s next era of play. As we grow across Europe and Latin America, we’re building The Playstack - the technology powering the next generation of sports, gaming, and fan experiences. Join us, and help make it the most widely used platform in the world From operations, to marketing, to product, we are looking for talented people who will shape how millions of customers play, watch, and connect every day.
The Staff Product Data Scientist sits at the intersection of product and strategy, operating as the decision engine for how Super uses data to drive commercial and product outcomes. Working in close partnership with Analytics Engineers, this role owns problem framing, hypothesis generation, and the translation of complex analyses into clear, actionable decisions — directly shaping how millions of customers engage with our products. This is a senior individual contributor role for someone who moves from messy business questions to sharp analytical conclusions that influence strategy at the VP and C‑suite level.
What The Role Involves
Be the Decision Engine
Conduct deep-dive analyses into customer behaviour, product usage, and commercial performance, going beyond surface metrics to uncover causal drivers through rigorous statistical methods.
Distinguish correlation from cause-and-effect, delivering decision memos with quantified impact ranges and clear “so what” guidance for senior stakeholders.
Shape Strategy with Data
Influence product roadmaps, commercial tactics, and marketing strategies by framing the right questions and designing experiments, including A/B tests and quasi-experiments.
Deliver evidence-based recommendations that directly change the course of decisions at Director, VP, and C‑suite level.
Define Metrics & Specifications
Design KPI trees, define metric intent, and specify requirements for Analytics Engineers to implement certified, production‑grade data products.
Define tracking plans for instrumentation, spe
📌 Staff Product Data Scientist (Madrid)
🏢 Super
📍 Madrid