Staff Product Data Scientist

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, specify dashboard requirements and acceptance criteria, and ensure data capture supports decision-making needs

Partner Across the Business

• Collaborate with Product Managers, Commercial leaders, Analytics Engineers, and Marketers, leading problem-framing sessions and challenging assumptions
• Reframe vague requests into testable hypotheses and bring analytical rigour to high-stakes discussions

Tackle Ambiguity

• Translate broad business challenges into sharp analytical problems with measurable outcomes, bringing structure to fast-moving, ambiguous environments

What we are looking for

• Advanced statistical knowledge and applied experience with causal inference methods: A/B testing, confidence intervals, regression, quasi-experimental designs (difference-in-differences, synthetic controls, regression discontinuity), and familiarity with CUPED and variance reduction techniques
• Proficiency in SQL for exploratory analysis and data validation (Snowflake environment)
• Demonstrated track record of producing decision memos, strategy recommendations, or business cases that have measurably influenced product and commercial strategy
• Ability to transform complex causal analyses into clear, compelling narratives for VP and C-suite audiences, making evidence accessible and actionable
• Strong commercial and product acumen — an understanding of how data connects to business outcomes, product design, and customer behaviour
• Comfort with ambiguity and a bias towards answering "what should we do?" rather than "what happened?"
• Minimum 5 years of relevant experience in analytics, data science, or product data science roles (ideally in product or commercial domains), with direct influence on strategy and growth through causal analysis and experimentation
• Degree in Data Science, Statistics, Economics, Computer Science, Engineering, Mathematics, or a related quantitative field — or equivalent professional experience

What we offer

• Medical / Health Insurance
• Open Annual Leave
• Employee Assistance Programme
• Training & Learning Development

Additional benefits vary by country and will be shared during the hiring process.