Senior/Lead Data Scientist-Fraud

Responsibilities

• Build and deploy ML models to protect Klarna’s customers from fraudulent activities (e.g. account takeover or identity theft fraud).
• Lead data science projects, from problem definition until deployment.
• Monitor, maintain, and retrain existing ML models in production.
• Explore, engineer, and test new potential features to help models in predicting fraud.
• Communicate with stakeholders on conceptual design, development, deployment, and risk control of the model, including writing documentation for external parties.
• Maintain the engineering platform/system used by the team to stay compliant with the company’s requirements.
• Proactive in exploring novel ML/AI products to detect fraud.

What You Will Do

• Build and deploy ML models to protect Klarna’s customers from fraudulent activities (e.g. account takeover or identity theft fraud).
• Lead data science projects, from problem definition until deployment.
• Monitor, maintain, and retrain existing ML models in production.
• Explore, engineer, and test new potential features to help models in predicting fraud.
• Communicate with stakeholders on conceptual design, development, deployment, and risk control of the model, including writing documentation for external parties.
• Maintain the engineering platform/system used by the team to stay compliant with the company’s requirements.
• Proactive in exploring novel ML/AI products to detect fraud.

Who you are

• Have an advanced degree (Master or Doctorate) in a quantitative field (e.g. statistics, computer science, engineering, mathematics, physics, or related fields).
• 5+ years of experience as a Data Scientist, ML Engineer, or related roles in the financial sector.
• 2+ years of experience working in fraud-related problem space.
• Experience in handling large sizes of customer data (e.g. >100 millions transactions with a few hundreds features).
• Deep proficiency in ML end-to-end process: conceptual design, model development, deployment in production, and monitoring, including pitfalls and tradeoffs to make.
• Deep understanding of business value to deliver: know when an ML solution is needed and when the model is good enough to be deployed for production.
• Good understanding of what metrics to use for monitoring and when to retrain ML models.
• Strong Python and SQL skills, including familiarity with ML modeling packages (e.g. scikit-klean, LGBM) and CI/CD or deployment tools (e.g. Docker, Jenkins, and uv).
• Familiarity with Github and AWS Cloud Computing (Sagemaker, Lambda, S3, Athena, etc).
• Ability to communicate effectively with Analysts, Engineers, and non-technical roles.
• Strong ability to translate business problems into analytical/technical solutions.
• Willingness to collaborate across different locations and time-zones (US and EU), but you will be working at common office hours in your time-zone. Traveling for one or two weeks per year may be needed to meet in-person with other group members.
• Eager to take ownership of a project and deliver results with minimal supervision.
• Agile to adapt to new changes in technology or engineering platforms used by the company.

Awesome to have

• Experience working in payment-related business, e.g. BNPL, credit card, or P2P transfer.
• Technical experience on utilizing Gen AI, Graph Networks, Anomaly Detection, or Behavioral Biometrics into production (beyond just prompting, fine-tuning, or proto-typing solutions).
• Familiarity with AI productivity tools for coding, e.g. Cursor or Github co-pilot.
• Familiarity with compliance and regulation around personal data privacy and model bias.
• Experience in mentoring junior data scientists.
• Experience with inferring the outcome of rejected orders due to fraud suspicion or credit unworthiness.