At Nova Credit, our mission is to power a more fair and inclusive financial system for the world. We are on our way to accomplishing this mission by rewiring the financial industry with better credit infrastructure, analytics, and workflows, enabling more people to get access to credit opportunities. Our cross-border credit product, Credit Passport®, cash flow underwriting product, Cash Atlas™, and income verification product, Income Navigator, are trusted by leading organizations like American Express, Verizon, HSBC, SoFi, Scotiabank, and Yardi to help them reach valuable new applicants from traditionally credit excluded populations. With support from investors Canapi Ventures, Kleiner Perkins, General Catalyst, and Index Ventures, as well as industry veterans from Goldman Sachs, JP Morgan, and Citi, Nova Credit is revolutionizing the way lending is done.
The Senior Data Scientist will be part of the Cash Atlas Data Science team within Nova Credit, where you will play an essential role in researching, designing, and building out our data science strategy, for our Cash Atlas product. Applying your critical thinking and analytical skills to data, traditional statistical modeling, and modern machine learning techniques, you will serve as a conduit to bring key functions together, including data partnerships, risk and analytics, product, engineering, and customer success, to develop and implement our core data systems. Working closely with our customer-facing teams, you will demonstrate the strength of our products through data analytics, and insights. Your role is to ensure that Nova Credit’s products deliver high-quality predictive risk signals; every initiative you work on will be critical to our team’s success!
This is a full-time, remote-friendly role reporting to the Director of Data Science. Candidates based in the New York City Metropolitan Area are strongly preferred.
RESPONSIBILITIES & KEY PROJECTS:
- Develop algorithms to unify bank transaction data from multiple banks and data vendors
- Develop models based on bank transaction descriptions and other consumer finance data
- Use bank transaction data to research and create variables used for credit risk modeling
- Develop underwriting/credit risk models using bank transaction data
- Collaborate with engineering teams to guide reliable deployments of data science solutions;
- Perform continuous research into new data sources, feature engineering, and modeling methodologies
- 4+ years of relevant quantitative modeling or model validation experience in consumer finance/credit risk
- MS in a quantitative field such as Statistics, Data Science, Economics, or Operational Research
- Experience with generalized linear models, machine learning methods, and hypothesis testing
- Fluent in Structured Query Language (SQL) and Python
- Experience with PySpark preferred
- Experience with alternative data preferred
- Above all, critical thinking skills, creativity, an open mind, and curiosity to explore complex data to extract usable information and insights!
Everyone is welcome at Nova Credit. We are an equal-opportunity employer where our diversity and inclusion are central pillars of our company strategy. We look for applicants who understand, embrace, and thrive in a multicultural and increasingly globalized world. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.