Data Scientist

What you will do

• Build, deploy, and maintain forecasting and analytics models. The scope includes the full P&L and balance sheet (e.g., transactions, revenue, receivables).

• Own the end-to-end model lifecycle: problem framing, data sourcing, feature engineering, modeling, validation, documentation, versioning, and monitoring.

• Design driver-based and hierarchical forecasts, and reconcile outputs across markets and products to ensure consistency between the P&L, balance sheet, and cash flow.

• Develop scenario, sensitivity, and stress-testing tools to support the annual plan, monthly forecasts, and in-month outlooks.

• Partner with teams across the company to translate business questions into measurable models and decisions.

• Productionize solutions in Klarna’s cloud environment (Python/SQL), automating reliable, reproducible pipelines with Airflow and Docker.

• Create clear narratives, dashboards, and variance bridges that explain model outputs and drivers to • finance leadership.

• Champion best practices in clean, maintainable code, data governance, and model risk controls.

Who you are

• A data scientist with an ML background, proficient in Python and SQL, and comfortable shipping production code in the cloud (AWS) with Git/CI.

• Skilled in forecasting methods – both classical and ML-based forecasting with experience tuning.

• Structured and execution-oriented; able to define problems, prioritize, and deliver end-to-end with high autonomy.

• A clear communicator who can simplify complex topics for non-technical stakeholders.

• Excited to learn from and contribute to a team experimenting with cutting-edge tools and AI agents, motivated to explore how such innovations enhance finance and analytics work.

• An academic background in a quantitative field (e.g., Mathematics, Physics, Engineering).

Awesome to have

• Experience in fintech/e-commerce or consumer finance; familiarity with payments, receivables, and funding mechanics.

• AWS experience (e.g., Batch, Lambda, Step Functions, EC2, S3) and workflow orchestration (e.g., Airflow); containers (Docker).

• MLOps practices for monitoring/backtesting, drift detection, and alerting; and experimentation design.

Please include a CV in English.

Curious to learn more about Klarna and what it’s like to work here? Explore our career site!