Senior/Lead Data Scientist -Credit

Klarna; Turbigo; In loco; EUR 45.000 - 70.000; Tempo pieno

Descrizione del lavoro

Klarna is seeking a talented individual to tackle technically interesting modelling problems in fintech. You will train large transformer-based models using real-world data, design tokenisation schemes, and manage the entire model lifecycle.

The ideal candidate will possess a deep understanding of transformer architectures, hands-on experience with Python and PyTorch, and thrive in a small, high-ownership team. If you have a background in ML infrastructure or large-scale financial data, you will stand out in this role.

Competenze

• Deep understanding of transformer architectures and sequence modelling.
• Hands-on experience designing tokenisation schemes for various feature types.
• Proficiency in Python, PyTorch, SageMaker, and Airflow.

Mansioni

• Work on modelling problems in fintech and training large transformer-based models.
• Design tokenisation schemes for numerical, categorical, and temporal features.
• Own the full model lifecycle from data preparation through to production.

Conoscenze

Transformer architectures

Tokenisation schemes

Python

PyTorch

SageMaker

Airflow

Descrizione del lavoro

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

Nice 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