Senior/Lead Data Scientist-Fraud
Pubblicato il 15-10-2026 - Klarna in Bardi
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.