Your Mission

Responsibilities

• Evaluate what to build from data: Explore new and existing data sources (e.g. Open Banking, credit files, behavioural signals) to evaluate the feasibility of feature and cross-sell opportunities that could meaningfully improve customer understanding, engagement, and value.
• Prove how features will work before they’re built: Run hands-on analyses, test runs, and worked examples using real data to validate that proposed features are viable, explainable, and valuable. Pressure-test logic, edge cases, and failure states before ideas reach engineering.
• Define features end to end: Translate validated ideas into clear, implementation-ready specifications, including feature logic, rules, assumptions, examples, and customer-facing implications. Turn this into high-quality briefs and Jira tickets that engineers can build with confidence.
• Shape data-led cross-sell decisions: Use customer data and eligibility signals to determine which products should be offered to which customers, and when. Define the logic and experience behind data-led cross-sell features and journeys, in partnership with product marketing.
• Measure impact and iterate: Own the success of the features you define, measured through adoption, repeat usage, retention uplift, and CLTV impact. Analyse post-launch performance, iterate where needed, and recommend stopping or reshaping initiatives that don’t deliver.

Requirements

• 5+ experience in data-heavy roles, ideally in internal tools, fintech, consulting with build responsibility, or a startup / scale-up with a strong analytical culture.
• Confident working with raw data, comfortable writing SQL, pulling tables, and forming views from first principles rather than relying on pre-built dashboards or second-hand analysis.
• Execution-focused and decisive, able to get deep into complex problems, make pragmatic calls with imperfect information, and move work forward without waiting for everything to be perfectly defined.
• Strong systems and product logic thinking, able to reason about feature rules, data flows, and edge cases, and work credibly with engineers without owning implementation details.
• Comfortable translating analysis into buildable work, producing clear written specs, worked examples, and Jira-ready tickets that de-risk delivery and support fast execution.
• Analytical mindset with strong bias to action, preferring structured problem-solving, real data, and learning in production over heavy rituals, brand-led thinking, or endless debate.

The culture