Parker's mission is to increase the number of financially independent people. We believe we can achieve this goal by building tools that enable independent business owners to scale their businesses profitably. Our first product combines a virtual credit card system with dynamic spending limits and software tooling to help merchants grow and optimize their profitability.

We are growing very fast -- in less than five months, we grew to millions in card volume. We have a significant waitlist of customers waiting to use our product. We are looking to expand our headcount quickly to support the demand. Our investors include Solomon Hykes (founder of Docker), Paul Buchheit (founder of Gmail), Paul Graham (founder of Y Combinator), Robert Leshner (founder of Compound Finance), and many more. We have raised over $40M from top-tier fintech investors.

Key Responsibilities:

• Develop insights and data visualizations to solve complex problems and communicate ideas to internal stakeholders.
• Partner with data Eng team to iterate on the most effective data structures for the organization
• Build predictive ML 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.
• Partner with data engineers to validate & deploy solutions in an efficient, sustainable & usable manner.
• Research new and enhanced model features to improve our risk decisions.
• Framing and project management of key data science projects with cross functional teams
• Hire and mentor junior members of the data science team

What You’ll Bring

• Bachelor’s degree in quantitative field (statistics, math, economics, physics, etc.)
• 5+ years of experience in roles related to data science, statistics, or machine learning along with 2+ years of experience as a data science manager
• At least 3 years Experience with a financial institution
• Experience with commercial and emerging databases, technologies, and languages
• Experience with applying statistics and machine learning methods to solve business problems
• Strong knowledge of R , SQL and Python.
• Ability to self-start and self-directed work in an unstructured environment, comfortable dealing with ambiguity and approaching new problems
• Excellent written and verbal communication to effectively understand business problems and communicate analysis