Senior/Lead Data Scientist-Fraud

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ph3Responsibilities /h3 ul liBuild and deploy ML models to protect Klarna’s customers from fraudulent activities (e.g. account takeover or identity theft fraud). /li liLead data science projects, from problem definition until deployment. /li liMonitor, maintain, and retrain existing ML models in production. /li liExplore, engineer, and test new potential features to help models in predicting fraud. /li liCommunicate with stakeholders on conceptual design, development, deployment, and risk control of the model, including writing documentation for external parties. /li liMaintain the engineering platform/system used by the team to stay compliant with the company’s requirements. /li liProactive in exploring novel ML/AI products to detect fraud. /li /ul h3What You Will Do /h3 ul liBuild and deploy ML models to protect Klarna’s customers from fraudulent activities (e.g. account takeover or identity theft fraud). /li liLead data science projects, from problem definition until deployment. /li liMonitor, maintain, and retrain existing ML models in production. /li liExplore, engineer, and test new potential features to help models in predicting fraud. /li liCommunicate with stakeholders on conceptual design, development, deployment, and risk control of the model, including writing documentation for external parties. /li liMaintain the engineering platform/system used by the team to stay compliant with the company’s requirements. /li liProactive in exploring novel ML/AI products to detect fraud. /li /ul h3Who you are /h3 ul liHave an advanced degree (Master or Doctorate) in a quantitative field (e.g. statistics, computer science, engineering, mathematics, physics, or related fields). /li li5+ years of experience as a Data Scientist, ML Engineer, or related roles in the financial sector. /li li2+ years of experience working in fraud-related problem space. /li liExperience in handling large sizes of customer data (e.g.



100 millions transactions with a few hundreds features). /li liDeep proficiency in ML end-to-end process: conceptual design, model development, deployment in production, and monitoring, including pitfalls and tradeoffs to make. /li liDeep 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. /li liGood understanding of what metrics to use for monitoring and when to retrain ML models. /li liStrong 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). /li liFamiliarity with Github and AWS Cloud Computing (Sagemaker, Lambda, S3, Athena, etc). /li liAbility to communicate effectively with Analysts, Engineers, and non-technical roles. /li liStrong ability to translate business problems into analytical/technical solutions. /li liWillingness 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. /li liEager to take ownership of a project and deliver results with minimal supervision. /li liAgile to adapt to new changes in technology or engineering platforms used by the company. /li /ul h3Awesome to have /h3 ul liExperience working in payment-related business, e.g. BNPL, credit card, or P2P transfer. /li liTechnical experience on utilizing Gen AI, Graph Networks, Anomaly Detection, or Behavioral Biometrics into production (beyond just prompting, fine-tuning, or proto-typing solutions). /li liFamiliarity with AI productivity tools for coding, e.g. Cursor or Github co-pilot. /li liFamiliarity with compliance and regulation around personal data privacy and model bias. /li liExperience in mentoring junior data scientists. /li liExperience with inferring the outcome of rejected orders due to fraud suspicion or credit unworthiness. /li /ul /p #J-18808-Ljbffr

Vedi l’offerta su Go Lavoro: golavoro.it/annuncio-lavoro/7ghmmu