Senior/Lead Data Scientist -Credit
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At a glance
Job overview
Klarna is hiring a Senior/Lead Data Scientist -Credit. The Senior/Lead Data Scientist will work on complex modeling problems in fintech, focusing on training large transformer-based models using real-world transactional events. This role involves designing tokenization schemes for various feature types and managing the full model lifecycle from data preparation to production. The scientist will translate research decisions into systems that influence machine learning operations across Klarna, working within a high-ownership team.
Key focus areas include Work on technically interesting modelling problems in fintech, Train large transformer-based models on transactional events, and Design tokenisation schemes for numerical, categorical, and temporal features.
Successful candidates bring Experience Owning Full Model Lifecycle, Experience With Large‑Scale Transactional Data, and Background In ML Infrastructure Or MLOps. Important skills include Transformer Architectures, Sequence Modelling, Designing Tokenisation Schemes, Python, PyTorch, and SageMaker. Preferred (not required): Triton Kernels, GPU-level Optimisation, ML Infrastructure, and MLOps.
Skills & qualifications
Skills
Qualifications
Full job description
What you will do You will work on some of the most technically interesting modelling problems in fintech, training large transformer-based models on long sequences of real-world transactional events. You will design tokenisation schemes for numerical, categorical, and temporal features, making deliberate decisions about vocabulary size, sequence length, and information compounding. Your work spans the full model lifecycle, from data preparation through to production and you will translate research decisions into systems that set the direction for how machine learning operates across Klarna. You will be part of a small, high-ownership team where what you build has genuine impact on the products Klarna ships. Who you are
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Deep understanding of transformer architectures and sequence modelling, with the ability to reason through architectural tradeoffs
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Hands-on experience designing tokenisation schemes for heterogeneous feature types: numerical, categorical, and temporal
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Proficiency in Python, PyTorch, SageMaker, and Airflow
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Experience owning the full model lifecycle, from training through to serving in production
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Comfortable working in a small, high-ownership team on open-ended technical problems
Awesome to have
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Experience with Triton kernels or GPU-level optimisation
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Broader ML background spanning areas beyond deep learning
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Experience with large-scale transactional or financial data
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Background in ML infrastructure or MLOps
Please include a CV in English Curious to learn more about Klarna and what it's like to work here? Explore our career site !
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