Senior Data Scientist

The Big Picture As a Senior Product & Growth Data Scientist, you'll be a key driver of how data shapes our product and growth decisions. You’ll combine product analytics, experimentation, and applied statistical / ML modelling to influence ZOE’s strategy, roadmap, and member experience. You’ll partner with product and growth teams to determine when descriptive analytics is sufficient and when predictive or causal models materially improve our ability to help members. We believe most decisions are reversible, so you'll balance statistical rigour with pragmatism — helping us move forward with ~70% evidence and iterating as we learn. The Squad At ZOE, we’re on a mission to empower people with the most advanced science and technology to transform their health. Data is at the heart of how we build products that deliver measurable health outcomes — and how we get those products in front of millions of people. You’ll sit within our Product & Growth Analytics team, a close-knit group of six experts where we pride ourselves on a diverse range of perspectives (our current split is 80% female and 20% male). You’ll work closely with Product Managers, Growth Marketers, Designers and Engineers. The team combines analytics engineering, experimentation, and product data science — and you'll have a meaningful voice in how we raise the bar. Your Mission Optimise the growth journey: Analyse our acquisition funnel — from ad spend through quiz, signup, and activation — to find where we’re leaking users and where small changes compound into big wins. Help us reach millions efficiently. Run high-quality experiments: Design and analyse experiments across product and growth surfaces. Bring statistical rigour — (sequential testing, CUPED, uplift) to help us learn faster without sacrificing quality. Shape strategy through metrics: Build and evolve metrics across acquisition, activation, engagement, retention, and monetisation. Surface the risks and leverage points that change what we prioritise. Predict & Influence User Behaviour: Use causal and inferential methods (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, conversion, and LTV. Collaborate with Leadership: Act as a trusted analytics partner for the growth domain. You’ll communicate complex insights to senior stakeholders (including the C‑suite), providing the data‑driven confidence needed to shape company strategy. Elevate our foundations: Work with dbt and our instrumentation layers to ensure the data we rely on is high-quality. You’ll help design data systems that serve multiple use cases across the team. Apply a "So What?" Filter: We value clarity over complexity. You’ll be empowered to choose the simplest effective solution and pivot quickly when a hypothesis is disproven. Your DNA 5+ years of experience in product analytics, growth analytics, data science where you have owned analytics domains and influenced product intent. Strong quantitative foundation — whether through a degree (Statistics, Maths, CS, Engineering, Physics, Economics, or similar) or a track record of working through genuinely complex quantitative problems. Deep proficiency in SQL and Python, with hands‑on experience in statistical modelling (e.g., regression, classification, or time‑to‑event analysis). Experience running rigorous experimentation and a clear point of view on what good looks like. Familiarity with LTV, churn, retention, or conversion modelling — and translating predictions into concrete product or marketing interventions. The ability to translate complex technical findings into clear, actionable stories for stakeholders. You are comfortable sharing your perspective, even when it challenges the status quo. You thrive in dynamic environments and are comfortable making recommendations with imperfect signals. Thrive in fast‑moving, low‑process environments; aligned with our #ActFast value and comfortable acting on ~70% evidence. While not required, we’d love to hear about any experience you have with: Subscription‑based business models or consumer‑facing products. Measuring the impact of marketing spend (MMM, attribution or incrementality). Working with mobile or app‑centric data environments. #J-18808-Ljbffr