Staff Product Data Scientist

At Super, data isn’t a support function. It’s at the heart of every major decision we make.

As a Staff Product Data Scientist, you’ll operate at the intersection of product and strategy, owning decision-making, problem framing, and hypothesis generation. Your work won’t sit in slide decks. It will directly shape how millions of customers engage with our products.

This is a role for a very special kind of data scientist: someone who loves hard problems, thrives in ambiguity, and gets energised by moving from messy questions to clear decisions that drive strategy.

You’ll be expected to not just report what happened, but to uncover why through causal inference, anticipate what’s next, and produce decision memos that influence what we should do about it.

You’ll work in close partnership with Analytics Engineers, who build and maintain production data products. Your role is to define what needs to be measured and why, while AE ensures scalable, trustworthy data infrastructure.

• Run deep-dive analyses into customer behaviour, product usage, and commercial performance.
• Go beyond surface-level metrics to uncover causal drivers behind phenomena using rigorous statistical methods.
• Distinguish correlation from cause-and-effect.

• Influence product roadmaps, commercial tactics, and marketing strategies by framing the right questions.
• Design and analyse experiments, including A/B tests and quasi-experiments.
• Deliver decision memos that change the course of decisions, with quantified impact ranges and clear “so what” guidance for VP and C-suite stakeholders.

• Design KPI trees and define metric intent.
• Specify requirements for Analytics Engineers to implement certified, production-grade data products.
• Define tracking plans for instrumentation.
• Specify dashboard requirements and acceptance criteria.
• Ensure data capture supports decision-making needs.

• Collaborate with Product Managers, Commercial leaders, Analytics Engineers, and Marketers.
• Lead problem-framing sessions.
• Challenge assumptions and reframe vague requests into testable hypotheses.
• Bring analytical rigour to high-stakes discussions at the Director and VP levels.

Super moves fast. The questions won’t always be clear. You’ll bring structure to chaos by translating broad business challenges into sharp analytical problems with measurable outcomes.

• Advanced statistical methods & causal inference: A/B testing, confidence intervals, regression, quasi-experimental designs (difference-in-differences, synthetic controls, regression discontinuity), and an understanding of CUPED and variance reduction techniques
• SQL proficiency for exploratory analysis and data validation (we use Snowflake). Enough to spec requirements for Analytics Engineers. Optimisation expertise is not required at this level.

A track record of producing decision memos, strategy recommendations, or business cases that measurably influenced product and commercial strategy. You care less about perfect models and more about driving measurable change. Your analyses are only successful if they move the needle.

You excel at transforming complex causal analyses into clear, compelling narratives. You can distil findings into “so what” recommendations for VP and C-suite audiences, making evidence accessible and actionable for strategic decision-making.

You don’t just crunch numbers. You understand how data connects to business outcomes, product design, and customer behaviour.

You don’t wait for perfectly-scoped requests. You reframe vague asks, propose alternative hypotheses, and drive analytical roadmaps based on business priorities. You’re comfortable with ambiguity and bias toward “what should we do?” over “what happened?”.

Minimum 5+ years of relevant experience in analytics, data science, or product data science roles (ideally in product or commercial domains) where you’ve directly influenced 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 work experience.