Senior/Lead Data Scientist - Credit
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At a glance
Job overview
Klarna is hiring a Senior/Lead Data Scientist - Credit. Klarna is seeking a senior/lead data scientist to tackle technically challenging fintech modelling problems, training large transformer models on extensive transactional event sequences and designing tokenisation schemes for diverse feature types, while overseeing the full model lifecycle and influencing machine learning practices across the company.
Key focus areas include Work on technically interesting fintech modelling problems, training large transformer-based models on long transactional sequences, Design tokenisation schemes for numerical, categorical, and temporal features, deciding vocabulary size and sequence length, and Own the full model lifecycle from data preparation to production, translating research decisions into ML systems.
Successful candidates bring CV In English. Important skills include Transformer Architectures, Sequence Modelling, Architectural Tradeoffs, Designing Tokenisation Schemes, Python, and PyTorch. Preferred (not required): Triton Kernels, GPU-level Optimisation, Broader ML Background, and Large-scale Transactional Data.
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 such as 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 !
klarna.com
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