Senior/Lead Data Scientist - Credit

Klarna; Greater London; On-site; GBP 60,000 - 80,000; Full time

Job summary

Klarna is seeking a talented individual to work on cutting-edge modelling problems in fintech. You will train transformer-based models on real-world transactional data and design tokenisation schemes for various features.

The ideal candidate will have deep expertise in transformer architectures and proficiency in tools such as Python, PyTorch, and SageMaker. You will be working in a small team, where your contributions will have a significant impact on Klarna's products.

Qualifications

• Deep understanding of transformer architectures and sequence modelling.
• Hands-on experience designing tokenisation schemes for heterogeneous feature types.
• Proficiency in programming languages and tools such as Python, PyTorch, and SageMaker.

Responsibilities

• Work on technically interesting modelling problems in fintech.
• Design tokenisation schemes for various feature types.
• Manage the full model lifecycle, from data preparation to production.

Skills

Deep understanding of transformer architectures

Experience with tokenisation schemes

Proficiency in Python

Experience with PyTorch

Experience with SageMaker

Experience with Airflow

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

• Deep understanding of transformer architectures and sequence modelling, with the ability to reason through architectural tradeoffs
• Hands‑on experience designing tokenisation schemes for heterogeneous feature types such as numerical, categorical, and temporal
• Proficiency in Python, PyTorch, SageMaker, and Airflow
• Experience owning the full model lifecycle, from training through to serving in production
• Comfortable working in a small, high ownership team on open ended technical problems

Awesome to Have

• Experience with Triton kernels or GPU‑level optimisation
• Broader ML background spanning areas beyond deep learning
• Experience with large-scale transactional or financial data
• Background in ML infrastructure or MLOps