Who we are:
At R2, we believe that small and medium businesses are the productive engine of society. Small and medium businesses (SMBs) make up over 90% of companies in Latin America, yet they face a trillion-dollar credit gap. Our mission is to unlock SMBs’ potential by providing financial solutions that are tailored to their needs. We are reimagining the financial infrastructure of Latin America - where SMBs financial needs are satisfied without ever having to go to a bank.
R2 enables platforms in Latin America to embed financial services that SMBs can then leverage (starting with revenue-based financing). We are a tight knit team coming from organizations such as Amazon, Google, Nubank, Uber, McKinsey & Co., Mercado Libre, Globant, and J.P. Morgan. We are backed by world-class investors such as Gradient Ventures (Google’s AI-focused fund), General Catalyst, Y Combinator, Femsa Ventures, PayU, among others.
As a Credit Risk Scientist, you will be a core member of R2’s Credit Analytics team, your role is to transform R2 into a world-class data-driven organization so that we can provide access to capital to millions of small businesses across Latin America. You will have meaningful responsibility to shape the company’s analyses, financial models and experiments, credit and data culture to deliver to our partners the best capital-as-as-service infrastructure.
What you’ll work on:
- Identify credit process weaknesses, emerging credit risk trends and issues, and gaps in the credit policy, including ad-hoc analysis.
- Work with data to solve our core credit business problems, apply analytical methods, and conduct end-to-end analyses, including data acquisition, pre-processing and modeling, and arriving at answers that can be automated.
- Use analytical and statistical techniques to measure and determine significance of credit terms, and help drive change in our overall credit strategy.
- Design new approaches and build predictive models (supervised and unsupervised) achieving the highest business impact.
- Help build the analytical infrastructure alongside the data engineers, including self-serve data pipelines and data models that make credit reporting and analysis much more efficient.
- Work alongside our ML scientists to enhance our risk scoring models and offer recommendations, brainstorming on different approaches and alternative data to use in production.
- Work closely with the data team in the deployment of credit models built into production.
- Document the methodology used and results obtained in a clear manner for reproducible research.
Who you are:
- You have at least 3 years of experience with data science at theoretical and practical levels.
- You have B2C or B2B predictive modeling experience gained from within the financial services.
- You have strong programming abilities (structured, object-oriented, and/or event-oriented programming) and are very comfortable programming in Python/R and SQL.
- You have strong statistical and mathematical knowledge, including linear algebra, calculus, and statistical inference, among other topics.
- You have built regression and/or classification models in the past for solving financial problems.
- You are comfortable consuming data through APIs, SFTP, or straight-up CSVs.
- You have deployed machine learning models in cloud architectures (AWS preferably) in the past.
- You have an owner mindset: you care about getting to the bottom of how to make a startup successful.
- You understand every problem as an opportunity for analyzing and learning.
> Does this position sound like a good fit? Apply at the following link:
https://r2.bamboohr.com/careers/37
R2
