Klarna
Lead Data Scientist - Credit Risk Modeling
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Role Overview
You will tackle some of the most technically advanced modelling problems in fintech, focusing on training large transformer-based models on long sequences of real-world transactional events. Your contributions will significantly impact Klarna’s product offerings.
Responsibilities
- Design tokenisation schemes for numerical, categorical, and temporal features, considering tradeoffs in vocabulary size, sequence length, and information retention.
- Integrate technical research decisions into scalable ML systems, influencing Klarna’s broader machine-learning strategy.
- Work across the full model lifecycle, from data preparation and training to deployment, knowing that your work directly shapes Klarna’s shipped products.
- Collaborate in a small, high-ownership team where your models have direct and meaningful impact.
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Requirements and Ideal Background
- Core Expertise:
- Deep understanding of transformer architectures and sequence-based modelling.
- Ability to reason через technical tradeoffs (e.g., efficiency vs. accuracy).
- Technical Skills:
- Hands-on experience with tokenisation for heterogeneous datasets (numerical, categorical, tikrajuri temporal).
- Proficiency in Python, PyTorch, SageMaker, and Airflow.
- Full ownership of model lifecycles, including training to production deployment.
- Preferred:
- Knowledge of Triton kernels or GPU-level optimisations.
- Experience beyond deep learning (e.g., broader ML methodologies).
- Work with large-scale transactional or financial datasets.
- Background in ML infrastructure or MLOps.
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Additional Notes
Applicants must submit a CV in English. General enquiries about Klarna’s cultural fit and working environment can be explored here.
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