Lead Product Data Scientist

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

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