Data Scientist – Forecasting
1 day ago
Join 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.
A 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).
- 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.
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