Senior/Lead Data Scientist -Credit

Klarna; Stockholms kommun; On-site; SEK 662,032 - 882,709; Full time

Job summary

Klarna is seeking a skilled professional to tackle challenging modelling problems in fintech, focusing on training large transformer-based models. You will design tokenisation methods for various data types and oversee the entire model lifecycle, making impactful contributions to Klarna's machine learning systems.

The ideal candidate has a strong understanding of transformer architectures, proficiency in Python, and experience working in a high-ownership team. Expertise in tools like PyTorch and SageMaker is highly desirable.

Qualifications

• Deep understanding of transformer architectures and sequence modelling.
• Hands-on experience with tokenisation for numerical, categorical, and temporal features.
• Experience with the full model lifecycle from training to serving.

Responsibilities

• Work on modelling problems in fintech, training transformer-based models.
• Design tokenisation schemes for various feature types.
• Translate research into systems for machine learning at Klarna.

Skills

Transformer architectures

Tokenisation schemes

Python

PyTorch

SageMaker

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: 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