Lead Growth Product Analyst

Own the analytics for the growth squad across acquisition, conversion, retention and lifetime value, and be the go-to person for what’s working and why.

Design, run and evaluate experiments on the product, propositions, offers and pricing. That includes A/B tests and holdouts that measure true incrementality, not just uplift of a subset of customers.

Leverage the Finance and Data Teams view of unit economics (CAC, contribution margin, LTV:CAC), adhering to the existing artifacts and definitions, and show how every initiative moves those numbers.

Analyse marketing channel mix and acquisition efficiency with the marketing team, including attribution and spend allocation.

Segment customers and prospects by persona, behaviour and expected value, and turn those segments into targeted propositions and journeys. Leveraging data science predictions where they can add value.

Track early-journey behaviour such as first-month engagement, commitment take-up and early retention, and find the levers that set patients up for long-term success.

Shape the growth roadmap with the Principal PM, sizing opportunities and prioritising by expected impact on tier-one metrics.

Present results, wins and failures to leadership and stakeholders clearly and regularly, with a recommendation every time.

Work with BI, data engineering, commercial and pricing, take ownership of a set of scorecard metrics, and be able to understand and articulate drivers to their movement and fluctuations.

Keep all analysis and experimentation inside clinical, data protection and advertising rules, working with the clinical and compliance teams where appropriate.

Several years in product, growth or marketing analytics, ideally in a subscription, D2C or digital health business.

A track record of analysis that changed decisions and moved commercial metrics, with clear examples.

Expert SQL and confident use of a BI tool such as Looker. Python or R for deeper analysis.

Strong experimentation skills, including test design, sample sizing, statistical significance, incrementality and pitfalls such as novelty effects and cannibalisation.

A solid grasp of subscription economics: cohorts, retention curves, CAC, contribution margin and LTV.

Experience with marketing measurement, such as attribution models, channel performance and spend efficiency.

Comfort with product analytics tools such as Amplitude or Mixpanel, and with funnel and behavioural analysis.

The ability to turn messy questions into a clear recommendation, and to explain it simply to non-technical audiences.

Commercial judgement and a bias to action: you’re comfortable deciding with imperfect data and saying when you were wrong.