7+ years of experience in product analytics, data science, or experimentation-heavy roles
Degree in a quantitative field (Statistics, Maths, CS, Engineering, Physics, Economics, or similar)
Deep fluency in SQL and Python
Hands-on experience with statistical modelling and applied ML, such as regression, classification, survival analysis, or time-to-event modelling
Experience building and validating LTV, churn or retention models, and translating predictions into concrete product or lifecycle interventions
Strong judgment around model complexity vs. business value—you know when a heuristic beats a black box
Comfort with messy, real-world data and imperfect signals
Ability to lead by influence, mentor others, and raise analytical standards
Clear, structured communicator to both technical and non-technical audiences
Thrive in fast-moving, low-process environments; aligned with our #ActFast value and comfortable acting on ~70% evidence
Responsibilities:
Lead product experimentation by introducing advanced statistical testing methods and platform improvements that deliver clear, confident insights for quicker decisions
Own and evolve core product metrics across activation, engagement, retention, and monetisation to identify risks and leverage points
Use causal and inferential thinking (e.g., uplift modelling, regression, survival analysis) to move beyond “what happened” to “why”
Develop lightweight ML models and segmentations that identify the specific levers driving long-term retention and growth
Set the standard for analytical methods and best practices across the team
Mentor analysts and lead by example - staying hands-on with data foundations (dbt/instrumentation) and showing the team how to turn raw data into influential narratives
Apply a "so what?" filter to every project, ensuring complexity is only added when it sharpens a decision, and iterating quickly when reality proves a hypothesis wrong