pIn this role you will 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, translating research decisions into systems that steer ML at Klarna. You join a small, high-ownership team where your work directly impacts Klarna’s products. This is a mission-driven opportunity to shape scalable ML infrastructure in fintech. /pulliTrain transformer-based models on long sequences of transactions /liliPlan and optimize vocabulary size, sequence length,



and information compounding /liliSupport end-to-end model lifecycle from data preparation to production serving /liliTranslate research decisions into production systems and ML direction for the company /liliDeep understanding of transformer architectures and sequence modelling /liliExperience designing tokenisation for heterogeneous features /liliProficiency in Python, PyTorch, SageMaker, and Airflow /liliExperience owning the full model lifecycle from training to serving in production /liliAbility to work on open-ended technical problems in a small team /liliautonomy /liliproblem-solving /lilitokenisation design for numerical, categorical, and temporal features /li /ul #J-18808-Ljbffr

📌 Lead Data Scientist - Credit Risk Modeling (Monza)
🏢 Klarna
📍 Monza