Lead Product Data Scientist

As a Lead Product Data Scientist, you will set the data science direction for our product organisation, combining product analytics, experimentation, and applied statistical / ML modelling to shape strategy, roadmap decisions, and member experience.

You'll help teams decide when descriptive analytics is enough and when predictive or causal models materially improve decisions. We believe most decisions are reversible, so you'll balance rigour with pragmatism-moving fast with ~70% evidence., * Raise the Bar in Experimentation: Lead product experimentation by introducing advanced statistical testing methods and platform improvements that deliver clear, confident insights for quicker decisions.

• Drive Product Strategy through Metrics: Own and evolve core product metrics across activation, engagement, retention, and monetisation to identify risks and leverage points.
• Predict & Influence User Behaviour: Use causal and inferential thinking (e.g., uplift modelling, regression, survival analysis) to move beyond "what happened" to "why." You'll develop lightweight ML models and segmentations that identify the specific levers driving long-term retention and growth.
• Elevate Analytical Excellence: Set the standard for analytical methods and best practices across the team. You will 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.
• Champion a Product-First Mindset: 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.

• 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.

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. You'll be part of the Product Analytics team, working closely with Product Managers, Designers, Engineers, and Nutrition Scientists. The team combines analytics engineering, experimentation, and product data science, and you'll play a leadership role-setting standards, mentoring others, and raising the bar for how data is used in product decisions., We know that "hiring processes" can sometimes feel like a black box. At ZOE, we aim for a process that is efficient, insightful, and enjoyable. It's a two-way street: we want to get to know the real you, and we want you to get a true feel for life at ZOE. 1. The "Meet & Greet" with Talent (45 min) First up, a deep-dive chat with one of our Talent partners. Think of this as a "look under the hood"-we'll explore your journey so far, what gets you excited about our mission, and make sure we're aligned on the essentials like compensation, logistics, and right-to-work. 2. The Hiring Manager "Strategy Session" (45 min) This isn't just a tick-box exercise; it's an intentional session where we talk shop. We'll dive into your technical approach and behavioural experience to see how you'll thrive in our team, while giving you a front-row seat to our engineering culture and vision. 3. The Remote Loop (The Final Stretch) We've grouped our final interviews into a "loop" (usually over Google Meet) to give us a 360-degree view of your brilliance. It consists of three distinct sessions