Senior Data Scientist

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$/yr - $/yr

Nova Credit is a credit infrastructure and analytics company that enables businesses to grow responsibly through alternative credit data. As a Consumer Reporting Agency (CRA), Nova Credit leverages its unique data infrastructure, compliance framework, and credit expertise to help lenders fill critical gaps in traditional credit analytics. The company transforms the fragmented universe of consumer financial data into compliant, actionable risk insights through a comprehensive platform designed to increase conversion through expanded coverage, speed, and reliability.

Leading organizations, including JPMorgan Chase, Pay Pal, HSBC, SoFi, Scotiabank, App Folio, and Yardi, work with Nova Credit to make smarter credit decisions through cash flow underwriting with Cash Atlas™, quickly verify income with Income Navigator, and reach new-to-country consumers with Credit Passport®. 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 in Forbes’ Fintech 50 in 2025 and Built In’s Best Places to Work, 2025.

The Senior Data Scientist role will be part of the Data Science team at Nova Credit, where you will play a crucial role in researching, designing, and developing 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 crucial to the company’s success. This is a full‑time, hybrid role reporting directly to the Head of Data Science, based in the New York Metropolitan Area.

• Research and develop credit risk predictive models based on cross‑border consumer performance data or bank transaction data.
• Research and develop bank transaction text classification models leveraging statistical models and/or large language models.
• Utilize bank transaction data to develop attributes for credit risk assessment, income verification, or other financial products.
• Research and develop models for document processing and tampering identification.
• 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.

• 4+ years of relevant experience in data science, quantitative modeling, or machine learning.
• Relevant PhD or MS preferred (Data Science, Computer Science, Statistics, Applied Statistics, Operations Research).
• Fluency in generalized linear models and machine learning methods and tools.
• Experience with hypothesis testing.
• Fluency using Python and SQL.
• Experience using PySpark and Polars.
• Preferred: NLP and document fraud experience.
• Preferred: consumer credit risk knowledge.
• Ability to break down new and complex problems into fundamental and solvable components.

IC3: $142,800 - $173,700
IC4: $165,750 - $201,600

We offer competitive compensation, equity grants, incentive compensation, professional development, flexible PTO, tenure rewards, employer‑subsidized medical, dental, vision plans, mental health and wellness benefits and a range of other perks.

Everyone is welcome at Nova Credit. We are an equal opportunity employer where 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.