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The Role
You will be the Analytical Engine driving our credit strategy and portfolio performance with Senior Product Management. As a Product Analyst, your primary focus is crafting deep-dive analyses for the high-leverage intelligence and product experimentation used to scale our credit products.
In this role you will be helping shape the credit roadmap. By uncovering hidden trends and surfacing leading risk indicators, you will tell the story behind the numbers, propose product improvements, empowering senior leadership s to make high-stakes, fast-paced decisions across the entire lending lifecycle.
This is a high-impactindividual contributor role in the Data team embedded within the Credit function and acting as a technical bridge between Data Engineering, Risk, and Commercial teams.
How You’ll Contribute
- Advanced Analytics & Modeling: Lead complex, ad hoc investigations into portfolio performance and credit risk drivers, using SQL to move beyond surface-level metrics.
- Product A/B Testing: propose and set up product experiments on Statsig to improve Credit product for our customers based on analytical findings
- Modern Data Stack Ownership: Design, build, and maintain robust data models in dbt, ensuring our credit data layer is scalable, version-controlled, and optimized for performance.
- Strategic Cohort & Valuation Analysis: Architect sophisticated cohort models to drive unit economics, portfolio valuation, and LTV projections that inform our long-term commercial strategy.
- Predictive Insights: Proactively identify emerging risks and growth opportunities, moving from "what happened" to "what will happen" through rigorous analytical reasoning.
- Stakeholder Influence: Deliver high-density deep-dives for strategic initiatives, presenting technical findings with clarity and commercial "so-what" to senior stakeholders.
- Cross-Functional Leadership: Partner with Decision Science and Product squads to ensure data-driven insights are seamlessly integrated into automated underwriting and product features.
- Regulatory Rigor: Maintain the integrity of our analytical frameworks, ensuring all outputs align with internal standards and FCA/CONC guidelines.
Who You Are
- Analytical Strategist: You don’t just pull data; you interpret it through a commercial lens to drive better credit outcomes.
- Technical Craftsman: You advocate for clean code, automated workflows, and the "Modern Data Stack" philosophy over legacy manual reporting.
- Precision-Focused: You understand that in credit, accuracy is non-negotiable. You bring a rigorous approach to data validation and financial logic.
- Effective Communicator: You can navigate a technical discussion with Data Engineers and then pivot to a strategic briefing with the Head of Credit or CFO.
- Fintech Native: You thrive in fast-paced environments, take full ownership of the analytical lifecycle, and prefer building scalable solutions over quick fixes.
What You’ll Bring
- Senior-Level Technical Stack: Expert proficiency in SQL and Python (Pandas, NumPy, etc.) for data manipulation and analysis.
- Modern Data Tooling: Hands-on experience working within a cloud data warehouse (e.g., Snowflake, BigQuery) and a strong mastery of dbt for data transformation and modeling.
- Fintech Credit Expertise: Significant experience within a modern UK fintech or high-growth consumer lender, with a deep understanding of the credit product lifecycle beyond traditional credit reporting.
- Strategic Modeling: A proven track record in conducting advanced cohort analysis, portfolio valuations, and financial modeling to support executive decision-making.
- Domain Knowledge: Fluency in credit portfolio KPIs, risk metrics (e.g., PD, LGD, EAD), and the nuances of credit bureau data.
Impact-Driven: Experience translating complex technical analysis into actionable product or risk interventions that have moved the needle on business performance.
The Interview Process
- Chat with a Talent Partner (30 mins)
- Behaviours & Culture Interview (45 mins)
- Technical Assignment (60 mins presentation)
- Final Interview (30 mins)
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