Remote Frontend Jobs

Overview

It’s an exciting time to join Superbet! We’re entering new markets, developing new technologies, and moving step by step towards our goal of exciting the world. As our business grows, the number of exciting people initiatives grows with it, and we’re looking for a new colleague to partner with our team to bring these to life.

The Role

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.

What You’ll Do

Be the Decision Engine

• 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.

Shape Strategy with Data

• 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.

Define Metrics & Specifications

• 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.

Partner Across the Business

• 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.

Tackle Ambiguity

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.

What We’re Looking For

Technical Mastery

• 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.

Decision Science Mindset

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.

Executive Communication

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.

Commercial & Product Acumen

You don’t just crunch numbers — you understand the business context and translate data insights into commercially meaningful recommendations.

Tagged as: machine learning, senior, snowflake, SQL