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 access 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. We were recently named Forbes' Best Startup Employers 2024 and Fast Company's World's Most Innovative Companies 2024.
Nova Credit offers a dynamic and inclusive work environment where you can meaningfully impact the lives of people historically excluded from the credit system. We value diversity, intellectual honesty, and innovation and are committed to supporting our team members' professional growth and development. If you're passionate about leveraging technology to drive financial inclusion, we want to hear from you! For a more in-depth look at our engineering culture, check out our page on Key Values!
The Senior Data Scientist role will be part of the Data Science team within Nova Credit, where you will play an essential role in researching, designing, and building out our global data science strategy. 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 and services through data analytics, and insights. Ultimately, your role is to ensure that Nova Credit’s products deliver high quality predictive risk signals; every initiative you work on will be critical for the company’s success!
This role is reporting into the Senior Director of Data Science. There is a location preference for a NYC-based candidate for this opportunity, however we are open to considering talent across the U.S. for a Remote position.
Job responsibilities:
- Develop credit risk predictive models based on cross-border consumer performance data or bank transaction data
- Develop models to categorize and group bank transactions to support multiple Nova Credit products
- Use bank transaction data to develop attributes for credit risk, income verification, or other products
- Collaborate with our engineering organization to guide reliable deployments of data science products; Perform continuous research into new data sources, variables, and modeling methodologies
- Develop and execute model monitoring solutions
YOUR SKILLSET
- 4+ Years Experience in data science, quantitative modeling, or machine learning in a bank or fintech
- Relevant PhD or MS preferred (e.g. Data Science, Computer Science, Statistics, Applied Statistics, Operational Research)
- Required - Fluency in generalized linear models & machine learning methods and tools;
- Preferred - Experience with hypothesis testing
- Required - Fluency using Python and SQL; Preferred - Experience using PySpark and Pola.rs
- Consumer credit risk knowledge is preferred
- Above all, the ability to break down new and complex problems into fundamental and solvable components
Everyone is welcome at Nova Credit. We are an equal opportunity employer where our diversity and inclusion are central pillars to 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.
