Prior Listings
Data Scientist
Data Scientist
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The Petal mission
Petal’s mission is to expand access to opportunity, by making responsible, modern financial services available to everyone. Founded in 2016, Petal provides technology-enabled credit cards to consumers who are historically underserved by mainstream providers.
Petal pioneered automated cash flow underwriting, a transformative new approach to assessing consumer creditworthiness with the potential to expand access to tens of millions of U.S. consumers without credit history, or for whom traditional credit scores do not tell the whole story. Petal pairs this groundbreaking, data-driven underwriting technology with a mobile-first, digitally native product experience designed to help users manage and build credit responsibly. For Petal, it’s a mission as much as it is a business—with a goal to reimagine finance for the next generation of consumers.
At Petal, we're looking for people with kindness, positivity, and integrity. You're encouraged to apply even if your experience doesn't precisely match the job description. Your skills and potential will stand out—and set you apart—especially if your career has taken some extraordinary twists and turns. At Petal, we welcome diverse perspectives from people who think rigorously and aren't afraid to challenge assumptions.
The Data Scientist role
The Data Science function is critical to Petal’s mission of making credit more broadly available to populations not well-served by traditional credit scores. We are working on revolutionizing credit through usage of personal cash flow in underwriting. Data Scientists at Petal build, maintain, and continuously improve consumer credit models using best-in-class techniques to inform our risk underwriting, acquisition, and customer management teams and strategies.
Key responsibilities
- Develop insights and data visualizations to solve complex problems and communicate ideas to internal stakeholders.
- Build predictive models from development through testing and validation for customer acquisition, underwriting and customer management.
- Extract and analyze data, investigate data integrity, generate metrics and perform ad hoc analysis.
- Explore and test new data sources to improve our risk and marketing models.
- Research new models and algorithms to improve our credit scoring.
- Research new and enhanced model features to improve risk models.
- Partner with data engineers to validate & deploy solutions in an efficient, sustainable & usable manner.
Characteristics of a successful candidate
- >2 years experience in data science building and implementing models; B.A. or M.S. degree in a STEM Major (Science, Technology, Engineering, or Math) or equivalent work experience is required.
- Strong knowledge of OLS and machine learning models.
- Strong knowledge of SQL and Python.
- Strong self-management, drive, and organization. Ability to multi-task in a fast-paced environment is essential.
- Experience in financial industry preferred.
We are an equal opportunity employer, and we are committed to building a team culture that celebrates diversity and inclusion. We’re proud to be different, together.
Petal provides standard ranges in order to be compliant with local legislation as well as to provide greater transparency to candidates. The compensation information provided here is based on a good faith estimate at the time of this job posting. The salary range for this position is $112,500 - $147,500. Ranges are based on function, level, and location, and are benchmarked against similar stage companies. Exact compensation amounts are determined by multiple factors including but not limited to skill set, level of experience, and office location, and may vary above or below from the amounts listed above.
The salary range listed is just one component of Petal’s total compensation package for employees. This position may also be eligible for equity awards, annual bonuses, short- and long-term incentives, and program-specific awards in accordance with the terms of Petal’s variable compensation plans.
This role is based in our Virtual Office (Remote), with the ability to align to either our NYC or Richmond (RVA) offices based on candidate location.
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This job is no longer open