Data Science / Quant Finance Intern
Access to credit is extremely scarce for the 40m gig economy workers in Brazil and in many other emerging economies. Volatile earnings and a work history that is difficult to access mean that these individuals cannot even apply for loans within most financial institutions and online lenders. When faced with emergencies or financial difficulties, gig workers struggle to find reliable alternatives.
Zippi solves that by building financial products tailored to gig workers. Our first product, a credit card for gig workers, is the first in Brazil to match workers' cash flows, giving customers much better control over their spending.
We see two powerful developments in consumer credit that allow Zippi to build a lasting competitive advantage. The first is customization, the ability to shape traditional credit products such as personal loans in a different way, that works radically better for a certain demographic. The second is smarter, long-term oriented pricing. Traditionally, loans in Brazil are priced considering only a handful of variables and the pricing is aimed at maximizing profit for that loan, with no real consideration of returning customers or cross-selling for instance.
The role of the Quant Finance Intern is to aid us implement these two pillars of competitive advantage in our risk assessment frameworks from day one. The Quant Finance Intern will support the development of pricing models, underwriting and collection policies. In practice, the mission involves setting the rules that’ll decide on how much we lend to whom and at what price. Ultimately, however, the vision is that the Quant Finance Intern is at the forefront of imagining the financial innovations that will make our products tailored to and priced for our users. Zippi must be treated as a laboratory of financial innovation , and adopting an inventive risk approach is central to that vision. She is to pursue it through responsible experimentation and savvy use of technology.
From June to August, we will be located at San Francisco to be accelerated by YC. It will be an intense and fun 3-month period where we intend to grow aggressively. In order to get there, we are transitioning from a quick-and-dirty MVP to a scalable lending operation.
Design the credit decision making model, using proprietary data as well as third-party purchased data Manage credit origination policies, based on the predictions of the credit decision making model. Create a systematic way of tracking portfolio performance, through the use of analytics and espousing market best practices Develop collections system in line with customer experience philosophy Work alongside data science team to uncover important quantitative insights Work alongside finance team to establish strategic goals based on unit economics optimization