Credit Genie is a mobile-first financial wellness platform designed to help individuals take control of their financial future. We leverage artificial intelligence to provide personalized insights and are building a financial ecosystem by offering tools and services that provide instant access to cash, and building credit. Our goal is to empower every customer to achieve long-term financial stability.
Founded in 2019 by Ed Harycki, former Swift Capital Founder (acquired by PayPal 2018). Backed by Khosla Ventures and led by industry pioneers from companies such as; PayPal, Square, and Cash App, we are well positioned to build the future of inclusive finance through cutting-edge technology and customer-centric solutions.

OVERVIEW

Come join our AI/ML and Data Science team at Credit Genie! We are seeking a technical Data Science leader with a solid background in Statistics and Machine Learning who is excited about solving hard Data Science problems and driving results while maintaining the highest bar for methodological rigor. In this role, you will have an opportunity to design and implement our company’s next generation of Risk and Underwriting models. These models are central to our business and have a direct impact on our customers every day. You will also design, execute, and evaluate new data acquisition processes as well as A/B tests and other experiments.

REQUIREMENTS

• A PhD in a relevant field (e.g., Mathematics, Statistics, …) and 4+ years of experience in a similar role OR Master degree in a relevant field and 8+ years of experience in a similar role.
• Advanced knowledge of Statistics (principles, methods, models, …).
• Proficient in SQL and Python (together with relevant libraries for Data Analysis, Statistical Modeling, Data Science, and Machine Learning).

• Set the company’s Data Science roadmap in collaboration with our leadership.
• Work in collaboration with a growing team of Data Scientists on the design and implementation of new Risk and Underwriting models.
• Generate actionable insights and models for other functional areas such as Marketing, Finance, Operations, and Customer Service.
• Collaborate with Product, Analytics, and Engineering leads.
• Set and maintain the highest methodological standards for Data Science across the company.
• Solve problems that have no clear existing solution and directly impact our customers.