About Us:

With over 85 million global active users and 2 million transactions per day, Klarna is on the way to becoming the world’s favorite way to shop. To help us get there, we’re assembling an unparalleled global talent team—accelerating individual careers, and disrupting entire industries. We’re looking for people ready to achieve the extraordinary and embrace our bold ambitions as we shape the future of payments and fintech. Will you join us?

What We 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.

• A data scientist with a 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 that actively experiments with cutting-edge tools and AI agents, and motivated to explore how such innovations can enhance day-to-day 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.

Closing

To ensure fairness and maintain global market competitiveness, each role in a specific location has a set base salary. During the recruitment process, we will assess your skills and experience to determine which role is the best fit for you.

Please include a CV in English.