Jessica Xu

• Working with customers who tend not to have substantial footprint with traditional credit bureaus, Octane supplements traditional underwriting with forward-looking cashflow analysis in which income is a key data element. • Validating an applicant’s trustworthiness via an innovative ML/AI solution to detect outliers who have mis-stated their income on application. • Using a smart workflow to automate income verification by triangulating the application income with various alternative data (payroll data, historical spend and payment patterns, earnings on previous employment) to stay true to the company’s mission of automated, smooth, frictionless application experience, with optimization to achieve the right ROI on the automation.

Experienced data scientist with over a decade's experience in leveraging various Machine Learning and analytical techniques to synthesize large amounts of data into evidence-based models and strategies to improve business efficiency and profitability.

Proven people leader with a track record of building, developing, and inspiring teams of data scientists of various sizes across geographic locations.

Accoladed collaborator who can bring people together across functional teams and across levels to drive enterprise level initiatives from inception to successful completion.

Substantial expertise in credit data and alternative data for consumers, merchants and businesses in the U.S.

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