Senior Data Scientist - Experimentation
Deliveroo · London - The River Building HQ
About this role
SENIOR DATA SCIENTIST Join us in our mission to transform the way people shop and eat, where impact, innovation and growth drive everything we do. Our Data Science organisation sits at the heart of Deliveroo powering decision-making across our 3 sided marketplace. Our Analytics community is highly active, with guest lecturers, study groups, mentorship programmes and a robust technical review process. Our data scientists come from all kinds of backgrounds but have excellence in common. We’re looking for a Senior Data Scientist to join our London team (working hybrid, 3 days in the office). In this role, you’ll enable high-quality human-led decision-making to maximise impact. WHAT YOU’LL BE DOING As a Senior Data Scientist at Deliveroo, your goal is to maximise business impact within one of our product areas. A core part of the role is designing, running and interpreting experiments, and using causal inference to guide product decisions in complex, real-world settings. You will go beyond simply answering “did this feature work?” and instead focus on understanding what the true impact is, why it worked or did not work, and how results generalise across users, markets and time. Here’s what your day-to-day might look like: - Driving Strategy/insights through Experimentation: Leading the design of both simple and complex experiments and proactively translating findings into strategic direction. - Master Causal Inference: Apply advanced techniques to estimate long-term and heterogeneous treatment effects. - Drawing reliable conclusions in settings with selection bias or imperfect randomisation. - Drive Deep Product Insights: Uncover the fundamental drivers of experiment performance, determining why features succeed and how results generalise across different users, markets, and timeframes. - Using insights from past experiments to improve future decision-making. WHAT YOU’LL NEED TO THRIVE - Strong experience designing, running and interpreting A/B experiments in production
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