Manager, Machine Learning Engineering (Fraud)

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  Set the technical and modeling strategy for fraud detection, aligning team efforts with key business outcomes such as fraud loss reduction, approval rates, and customer experience



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  Lead a team of machine learning engineers to design, build, and iterate on high-impact fraud models across the full ML lifecycle, from experimentation to production



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  Drive the evolution of modeling approaches, including the adoption of representation learning, transformer-based methods, and other advanced techniques for modeling complex behavioral data



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  Partner cross-functionally with Product, Fraud Analytics, Risk, and Engineering to define solutions, evaluate trade-offs, and ensure models are effectively integrated into decisioning systems



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  Develop talent by coaching engineers, providing feedback, and fostering a high-performing team culture grounded in technical excellence and ownership

What we look for

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  Bachelor’s in a technical field with 8+ years of industry experience, including 3+ years managing engineers



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  Experience with modern ML approaches, including representation learning, deep learning, or transformer-based models, as well as traditional methods such as gradient-boosted trees



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  Proven ability to lead teams delivering end-to-end ML solutions in production environments, including experimentation, evaluation, and model iteration in production



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  Strong engineering fundamentals and experience working with scalable systems and data pipelines



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  Track record of effective cross-functional collaboration with product, analytics, and engineering partners



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  Ability to operate in ambiguous, fast-evolving environments and drive clarity, prioritization, and execution



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  This position requires either equivalent practical experience or a Bachelor’s degree in a related field.