Klarna
Senior/Lead Data Scientist -Fraud
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What You Will Do
We are rebuilding our fraud detection systems from the ground up, and we are looking for data scientists who want the full mandate to do it right, from raw data to production model, at a scale affecting hundreds of millions of transactions.
Klarna is a big company that still moves like a startup: fast decisions, real ownership, and models that ship. We own the full stack and expect you to build it with us to enable your use cases. If you have the curiosity to find the right problem, the grit to build the right solution, and the ambition to see it matter, this is the opportunity.
Fraud is one of the most technically demanding problem spaces at Klarna. You will build best-in-class machine learning systems from the ground up, owning the complete pipeline from:
- Raw data
- Feature engineering
- Model design
- Training
- Real-time production deployment
A key part of this role is converting some of Klarna's existing rules-based fraud systems into sophisticated, model-driven architectures that operate at scale across hundreds of millions of transactions.
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You will build infrastructure from scratch, not maintain or extend existing frameworks. Working with engineers, analysts, and commercial stakeholders, you will:
- Translate ambiguous business problems into precise technical solutions.
- Bring novel approaches such as graph networks, anomaly detection, and behavioural signals into production, generating real impact.
Who You Are
- End-to-end ML ownership: Full stack, from data engineering to feature engineering, model design, training, low-latency production deployment, monitoring, and retraining.
- Strong instinct for when a model is ready for production and when it is not.
- Proven track record of building ML models and pipelines from scratch, not integrating or extending someone else’s tools.
- Experience building real-time or near-real-time inference systems—batch pipelines are insufficient.
- Comfortable working with large-scale datasets, including hundreds of millions of transactions and high-dimensional feature spaces.
- Strong skills in Python and SQL, with hands-on experience in:
- scikit-learn
- LightGBM
- Docker
- Jenkins
- Modern Python packaging
- Self-motivated, fast-moving, and creative: You bring novel solutions where others default to off-the-shelf tooling.
- Ability to communicate clearly across technical and non-technical audiences, including senior leadership.
- Degree in:
- Computer Science
- Physics
- Applied Mathematics
- Astrophysics
- Automatic Control
- Mathematics
- Software Engineering
- Electrical Engineering
- A related quantitative field
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Awesome to Have
- Experience building end-to-end ML systems in early-stage startups or small greenfield teams (a strong positive signal).
- Hands-on production experience with:
- Graph Neural Networks
- Anomaly Detection
- Behavioural Biometrics (beyond prototyping or fine-tuning)
- Familiarity with AWS (SageMaker, Lambda, S3, Athena) and CI/CD practices.
- Experience mentoring or technically guiding other data scientists.
Please include a CV in English.
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