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You will architect, code and deploy ML models (from scratch) to predict credit risk.

– Design, run, and analyze A/B and multivariate tests to test hypotheses aimed at optimizing user experience and portfolio risk.

– You will perform data exploration and build statistical models on user behavior to discover opportunities for decreasing user defaults. And you must truly be excited about this part.

– You’ll use behavioral and social data to gain insights into how humans make financial choices

– You will spend a lot of time in building out predictive features from super sparse data sources.

– You’ll continually acquire new data sources to develop a rich dataset that characterizes risk.