Requirements
- 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.
