Data Scientist

Description du poste:

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.
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