THE MISSION

Behind every purchase paid in installments at one of our merchants there is a credit decision we make and own. The Credit Risk team is responsible for those decisions: who we approve, how much we extend, on what terms, and how the portfolio performs afterwards.

We're looking for a Credit Risk Data Analyst to join the team in Barcelona. The portfolio we manage is growing, BNPL is coming into the European regulatory perimeter, and we're building the team and the tooling to match. You'll work with the CRO, with Data Science and Data Engineering, and with Commercial, and you'll have the opportunity to fully own parts of the portfolio from early on.

WHAT YOU WILL DO

Portfolio strategy

• Manage the credit strategy of a consumer portfolio built on millions of customers and thousands of merchants across multiple European countries: how much we extend, to whom and through which merchants. You'll work with Commercial and Account Management so that risk appetite and growth pull in the same direction.
• Review merchants and segments, decide where the portfolio should be adjusted, and make it happen.

Decisioning and changes

• Take credit decisions from analysis to production. Rules, credit limits, thresholds: propose the change, size it, test it, ship it, and measure what happened.
• You take the decisions, not only prepare them. You'll hold a clear mandate on merchant and portfolio decisions from the start, with the team behind you and room to grow it.

Monitoring and tooling

• Build the monitoring that tells us whether the portfolio and our decisions are doing what we expect, with risk-adjusted profitability as the organising metric.
• Monitor the portfolio across customers, merchants and countries, and dig into what's moving before it turns into losses.
• Build the team's knowledge base as you go: the decisions we take, the changes we ship and the reasoning behind them.

Collaboration

• Work with Data Science and Data Engineering on the models, features, pipelines and data that feed our decisions. We're aiming for a self-sufficient team on the data stack.
• Work with the rest of the Risk function, Fraud included, on the patterns that drive losses and what to do about them.

As you settle in, you'll take on a project of your own end to end.

WHO WE ARE LOOKING FOR

Must have

• A degree in engineering, physics, computer science, mathematics, statistics, economics or another quantitative field, or equivalent demonstrated quantitative ability.
• 3+ years in an analytical role where your output drove decisions.
• Strong SQL. You use it every day, you're comfortable with complex joins and window functions, and you can read someone else's query and work out why two numbers disagree.
• Some exposure to Python, at work or in your own projects. You can read a pandas script and adapt it to get to a result. You don't need to be a software engineer.
• Proactivity and technical curiosity. Our domain is technical: decisions live in code, configs and pipelines, and the people who do well here go and look at how things work rather than waiting to be told.
• Clear communication. You can explain a complex analysis to different audiences, from Commercial to leadership, and you can stand behind your conclusions.
• Fluent English (B2+ level). We use it on a daily basis and this is our global internal language.

Nice to have

• Exposure to consumer credit, BNPL, lending or e-commerce risk.
• Familiarity with git (or another version control system), CI/CD and how pipelines and code get released to production.
• dbt, or other analytics engineering and data modelling experience.
• Experience with experiments, control groups or A/B testing.

No credit or BNPL background? That's fine. We'll teach you the domain. What we look for is rigour with data and curiosity about how things work.

WHY YOU SHOULD JOIN SCALAPAY

• Attractive packages based on skills and experience. The salary band for this position is 32.000 - 44.000.
• Your analysis turns into decisions, and you see the results.
• Exposure to the full credit lifecycle, from approval to portfolio performance.
• A technical environment: you work close to the decision engine and the data pipelines, not only on their outputs.

OUR HIRING PROCESS

1. An initial chat with our Talent team
2. An interview with the Hiring Manager, to dive into your experience and understand your motivation
3. A case study with the Hiring Manager and another Risk team lead
4. A final chat with Simone, our CEO