Senior/Lead Data Scientist-Fraud

16 ott - Italia
Altro

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

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