Senior Data Scientist - Transformer Risk Modeling

Klarna

Monza

In loco

EUR 70.000 - 110.000

Tempo pieno

4 giorni fa
Candidati tra i primi
Generatore di candidature

Ottieni una risposta da questo datore di lavoro — un curriculum e una lettera di presentazione personalizzati, che corrispondono esattamente a ciò che stanno cercando.

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Descrizione del lavoro

Klarna is seeking an ML Engineer to tackle technically challenging modelling problems in fintech by training transformer-based models on long sequences of real-world transactional events. You will design tokenisation schemes for numerical, categorical, and temporal features, shaping vocabulary size, sequence length, and information compounding.

You’ll oversee the full model lifecycle from data prep to production, guiding ML direction for the company.

Competenze

  • Experience building transformer-based models in production settings.
  • Experience designing tokenisation for heterogeneous features.
  • Proficiency in Python and PyTorch for model development.
  • Hands-on experience with SageMaker and Airflow for ML workflows.
  • Track record of end-to-end model lifecycle from data prep to serving.
  • Strong problem solving and autonomous in small team settings.

Mansioni

  • Train transformer-based models on long sequences of transactions.
  • Plan and optimize vocabulary size, sequence length, and information compounding.
  • Support end-to-end model lifecycle from data preparation to production serving.
  • Translate research decisions into production systems and ML direction for the company.
  • Demonstrate deep understanding of transformer architectures and sequence modelling.

Conoscenze

Transformer models
Tokenisation design
Python
PyTorch
SageMaker
Airflow
Production deployment
Fintech domain
Problem solving
Autonomy

Descrizione del lavoro

Klarna is seeking an ML Engineer to tackle technically challenging modelling problems in fintech by training transformer-based models on long sequences of real-world transactional events. You will design tokenisation schemes for numerical, categorical, and temporal features, shaping vocabulary size, sequence length, and information compounding.

You’ll oversee the full model lifecycle from data prep to production, guiding ML direction for the company.

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