Staff Machine Learning Engineer, Fraud & Abuse
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Die Stelle
Lead the design, development, and scaling of real-time and batch ML systems for fraud and abuse detection.
Build and maintain data pipelines and APIs to support ML model inference at scale.
Collaborate with product and engineering teams to define data models and feature store enrichments.
Integrate diverse internal and third-party data sources to enhance feature store and modeling capabilities.
Ensure data quality through automated validation, monitoring, and alerting; develop new triggers and risk signals.
Drive experimentation, SEV readiness, and downstream integration of risk decisions, applying ML and engineering best practices to ML-platform solutions.
Build and maintain data pipelines and APIs to support ML model inference at scale.
Collaborate with product and engineering teams to define data models and feature store enrichments.
Integrate diverse internal and third-party data sources to enhance feature store and modeling capabilities.
Ensure data quality through automated validation, monitoring, and alerting; develop new triggers and risk signals.
Drive experimentation, SEV readiness, and downstream integration of risk decisions, applying ML and engineering best practices to ML-platform solutions.
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