About the RoleJoin our team in Warsaw, where we’re collaborating on a cutting-edge fintech venture with a global industry leader. Together with our Partner – Klarna, we’re building an IT hub designed to drive innovation in digital payment solutions. We’re on the lookout for top-tier engineers who thrive in dynamic, forward-thinking environments. Spyrosoft is leading the recruitment process, facilitating a seamless experience for candidates who are ready to shape the future of online shopping and payments.We are looking for a Data Scientist with strong forecasting expertise to join Klarna’s Group FP&A team. In this role, you will design, build, and deploy forecasting and analytics models that power Klarna’s financial planning and analysis across the full P&L and balance sheet (transactions, revenue, receivables, etc.).You’ll own the end-to-end model lifecycle – from problem framing and data sourcing through feature engineering, modeling, validation, documentation, and monitoring. Your work will support decision-making at the highest levels, ensuring consistency across financial statements and enabling scenario, sensitivity, and stress-testing analyses for plans and forecasts.This is a hands-on role with direct business impact: you’ll productionize solutions in the cloud (AWS) with Python, SQL, Airflow, and Docker, and communicate insights clearly to finance leadership.Responsibilities Develop, deploy, and maintain forecasting and analytics models covering P&L, balance sheet, and cash flow. Own the end-to-end model lifecycle: problem definition, data sourcing, feature engineering, model design, validation, versioning, and monitoring. Design driver-based and hierarchical forecasts; reconcile across markets and products for financial consistency. Build tools for scenario, sensitivity, and stress testing to support annual plans, monthly forecasts, and in-month outlooks. Partner with stakeholders across Klarna to translate business questions into measurable models and actionable insights. Automate robust, reproducible data pipelines in Klarna’s cloud environment (AWS) using Python, SQL, Airflow, and Docker. Create clear narratives, dashboards, and variance bridges that explain model outputs to finance leadership. Promote best practices in clean, maintainable code, data governance, and model risk management. Who You AreA data scientist with an ML background, proficient in Python and SQL, and experienced in shipping production code in the cloud (AWS) with Git/CI. Skilled in forecasting methods, both classical and ML-based, with hands-on experience tuning and validating models. Structured, execution-driven, and comfortable delivering end-to-end solutions with high autonomy. An effective communicator who can simplify complex models for non-technical stakeholders. Curious and forward-looking, eager to experiment with cutting-edge tools and AI agents to enhance financial analytics. Academic background in a quantitative discipline (e.g., Mathematics, Physics, Engineering). Nice to Have Experience in fintech, e-commerce, or consumer finance, with knowledge of payments, receivables, or funding mechanics. Hands-on experience with AWS services (e.g., Batch, Lambda, Step Functions, EC2, S3). Knowledge of workflow orchestration (Airflow) and containerization (Docker). Familiarity with MLOps practices (monitoring, backtesting, drift detection, alerting). Experience with experiment design and statistical testing.