Posted 17 days ago
No clicks
Work on building, deploying, and maintaining machine learning models to detect and prevent fraud (e.g., account takeover, identity theft) across Klarna’s products. Drive end-to-end data science projects from problem definition through production deployment and ongoing monitoring, including feature engineering and model retraining. Collaborate with stakeholders, engineers, and analysts to design risk controls, document models, and keep engineering platforms compliant. The role requires strong Python and SQL skills, familiarity with ML libraries and AWS, and at least 2 years of relevant experience plus an advanced quantitative degree.
- Compensation
- Not specified
- City
- Not specified
- Country
- United States
Currency: Not specified
Full Job Description
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).
Independently drive 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 predict fraud or increase conversion.
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.
Explore novel ML/AI solutions 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).
2+ years of experience as a Data Scientist, ML Engineer, or related roles, preferably in the financial sector.
Proficiency in ML end-to-end process from conceptual design to model development, deployment, and monitoring.
Good 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 model monitoring.
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.
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.
Willingness 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 fraud-related problem space, cyber security, and/or payment-related business, e.g. BNPL, credit card, or P2P transfer.
Experience in handling large sizes of customer data (>100 millions transactions with a few hundred features).
Technical experience on utilizing Gen AI, Graph Network, 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.
