Klarna
Senior/Lead Data Scientist - Open Banking

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What You Will Do
As a Senior Data Scientist within our Open Banking team, you will build the analytical foundation that turns raw bank account data into reliable, actionable insight for fraud prevention and underwriting decisions. You will engage in feature engineering across incoming and outgoing cash flows to model affordability, and develop machine learning models that estimate probability of default and produce fraud scorecards. You will own the full lifecycle of these models, from raw data through to production deployment, working closely with engineering to operationalize your work at scale. You will also help monitor model performance, impact, and acceptance rates through dashboards you help build, and you will partner closely with the Credit Modeling and Fraud teams to align on shared goals and infrastructure.
Reasons to use Rodeo
I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
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Graduate Consultant — 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
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Why you're a good match
You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
Who You Are
- Experience building and deploying machine learning models end to end, from raw data to production
- Strong feature engineering skills, ideally with transactional or financial data
- Proficient in Python and SQL, with experience working in AWS environments
- Solid understanding of classification modeling techniques for risk or fraud use cases
- Comfortable partnering closely with engineering teams to bring models into production
- Strong communication skills, with the ability to work cross-functionally with Credit Modeling and Fraud teams
- A curious, ownership-driven mindset, comfortable building infrastructure and processes from scratch


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Awesome to have
- Experience with open banking data or affordability modeling
- Familiarity with building dashboards for model monitoring, ideally with tools like Databricks
- Experience with monitoring and observability tools such as Datadog
- Background in underwriting, credit risk, or fraud detection use cases
Please include a CV in English
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