Employer: Klarna
Location: Warsaw (hybrid work: 2-3 days in the office)
Rate: 170 - 195 PLN per hour
Cooperation model: B2B or Employment Contract
Start date: ASAP
Duration: long-term agreement
Recruitment process: 1 timed Abstract and Logical Reasoning Test (up to 15 minutes); 1 short interview (up to 30 minutes)
Do you want to play a key role in revolutionizing the future of finance? Join Klarna - a global leader in deferred payments, with over 85 million active users and 2.5 million transactions processed daily! As a pioneer in modern payment solutions, Klarna is developing innovative methods that streamline the shopping experience, enhance transaction security, and expand the availability of purchasing options.
Your role is:
- > To build, deploy, and maintain forecasting and analytics models as part of Klarna’s Group FP&A team. The scope includes the full P&L and balance sheet (e.g., transactions, revenue, receivables);
- > To own the end-to-end model lifecycle: problem framing, data sourcing, feature engineering, modeling, validation, documentation, versioning, and monitoring;
- > To 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;
- > To develop scenario, sensitivity, and stress-testing tools to support the annual plan, monthly forecasts, and in-month outlooks;
- > To partner with teams across the company to translate business questions into measurable models and decisions;
- > To productionize solutions in Klarna’s cloud environment (Python/SQL), automating reliable, reproducible pipelines with Airflow and Docker;
- > To productionize solutions in Klarna’s cloud environment (Python/SQL), automating reliable, reproducible pipelines with Airflow and Docker;
- > To create clear narratives, dashboards, and variance bridges that explain model outputs and drivers to finance leadership;
Who You Are:
- > 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;
- > Working proficiency and communication skills in verbal and written English.