Senior Machine Learning Engineer (Research Scientist, Fraud)

Join Plaid's Fraud Data team as a Senior Machine Learning Engineer (Research Scientist, Fraud). In this role, you will lead applied research to develop next-generation fraud detection models, design rigorous experiments, and translate promising research into production-ready solutions. You will collaborate with a highly skilled, cross-functional team and leverage Plaid's network-level financial data to uncover insights and develop solutions that help consumers achieve greater financial freedom.

• Lead applied research to develop next-generation fraud detection models across various data types.
• Design rigorous experiments and evaluation methodologies that reflect real-world fraud dynamics.
• Translate promising research into production-ready solutions and communicate findings to advance the technical bar for fraud machine learning.

• Demonstrated scientific rigor, with strong written and verbal communication skills and the ability to clearly communicate complex research findings
• Strong proficiency in Python and experience building high-quality research prototypes that can inform or transition into production systems
• PhD in Machine Learning, Artificial Intelligence, Computer Science, Statistics, Applied Mathematics, or a closely related field strongly preferred. Candidates without a PhD may be considered with equivalent research experience, such as significant publications, patents, or widely adopted research contributions in relevant fields
• 2–4+ years of relevant industry or research lab experience, ideally post-PhD, with demonstrated research leadership and a track record of translating innovative research into measurable product or business impact
• We recognize that strong qualifications can come from both prior work experiences and lived experiences. We encourage you to apply to a role even if your experience doesn't fully match the job description. We are always looking for team members that will bring something unique to Plaid!
• Experience in fraud detection, security, risk, or abuse prevention
• Experience with large-scale training, graph systems, and sequential modeling