The Petal mission
Petal’s mission is to bring financial opportunity and innovation to everyone.
We're pioneering a new approach to credit, enabling Petal Card* applicants to leverage their banking history, in addition to their credit history, to establish their creditworthiness. Our proprietary Cash Scoring technology takes applicants’ income, spending, and savings into account and is helping traditionally underserved consumers across the United States access honest, simple and responsible credit, even if they’ve never had it before.
We bring the same ingenuity to Petal Card products. Our simple and intuitive app gives members access to credit score tracking, budgeting tools, subscription management, and automated payment options—everything they need to make financial progress.
Now more than ever, Americans need help improving their credit safely, responsibly, and affordably. If this sounds like something you’d like to be a part of, apply now, and let’s change this trillion-dollar industry together.
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.
*Petal Cards are issued by WebBank, Member FDIC
We are working on revolutionizing credit through usage of personal cash flow in underwriting. Achieving this objective requires best in class models that are continuously being improved making the data science function critical for this mission. These models will support our risk underwriting, acquisition and customer management teams. That’s why, at Petal, we’re launching a search for a highly-experienced Director of Data Science from the credit card industry to join our talented and mission-driven team. The ideal candidate has a successful track record of growing and overseeing a Data Science function in consumer credit, has organized and managed team members, and has the ability to imbue a fast-moving startup culture with an appreciation and reliance forward thinking machine learning decision modeling.
- Manage a team of data scientists
- Developing and deploying machine learning models for customer acquisition, underwriting and customer management.
- Motivating and mentoring the data scientists and modelers to deepen their understanding and use of cutting edge data science and machine learning methods
- Research and explore new data sources to improve the information sources for our proprietary models.
- Research new models and algorithms to improve our ML models.
- Build and establish model governance to identify any opportunities in our models early on
- Develop insights and data visualizations to solve complex problems and communicate ideas to internal stakeholders.
- Extract and analyze data, investigate data integrity, generate metrics and perform ad hoc analysis.
- Partner with data engineering leadership to validate & deploy solutions in an efficient, sustainable & usable manner.
Characteristics of a success candidate
- >2 years of managing a small/medium team of data scientists
- Experience working with business teams internally to influence strategy and roadmap for the data science team
- You can communicate complex ideas clearly and you manage expectations with your broader cross-functional stakeholders and leadership team
- You have an ability to understand the customer and business problem that is to be solved and can drive towards impactful outcomes
- >5 years experience in data science building and implementing models; Degree in a STEM Major (Science, Technology, Engineering, or Math) preferred
- Strong knowledge of traditional and machine learning models
- Strong knowledge of SQL , Python & R
- Strong self-management, drive, and organization
- Ability to multi-task in a fast-paced environment is essential
- Experience in the financial industry and specifically consumer lending preferred
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