Credit Risk Scientist Jobs (NOW HIRING)

One out of every two small businesses fails within their first five years, most often due to running out of cash. QuickBooks Capital is on a mission to make a dent in that statistic, by providing small businesses access to the capital they need when they need it, leveraging the data inside QuickBooks for faster and better decisioning. This way, our customers never again have to worry about not making payroll or saying no to a business opportunity. That’s how we power prosperity.

QuickBooks Capital is a nimble and high-priority business unit within Intuit that is looking to reinvent small business borrowing. We are the fastest growing SMB lending business in the market. We are looking for top talents and team members that love new challenges, cracking tough problems and working cross-functionally. If you are looking to join a fast-paced, innovative and incredibly fun team, then we encourage you to apply.

The role as QuickBooks Capital credit risk lead analyst will dive into three main areas to help the business grow:

• Drive new lending business initiatives from the credit risk expert perspective, especially, cash flow based lending products. 
• Ensure our loan portfolio is within our risk tolerance while growing the portfolio.
• Partner with our data science and data engineering teams to leverage the latest technology and models to serve our QuickBooks customers’ financing needs

Responsibilities

• Build great ownership and leadership by working collaboratively with business partners (product management, product development, marketing, data engineering, compliance, underwriting team etc.) to design new lending offerings to increase our market share. 
• Ensure new product credit underwriting platform and data infrastructure requirements and delivery road maps are robust and complete for product partners.
• Build and lead a high-performing team of credit strategists and analysts to deliver the target risk adjusted returns. 
• Partner closely with our marketing, product, data scientist, underwriting, partnership teams to drive customer segmentation, develop innovative credit strategies, and perform portfolio analyses
• Dive deep into our lending portfolio performance and design credit policy proposals that will improve business KPIs
• Leverage cloud data technology to extract insights from extensive transactional data and convert credit risk analyses into actionable business opportunities.
• Develop, deploy, and oversee tests that explore new methodologies and estimate key metrics for our acquisition strategies and credit policy
• Proactively contribute innovative concepts for the creation of new financing products tailored for QuickBooks customers.

Qualifications

• 12+ years working experience in lending or fintech related industries.
• 4+ years team management experience in leading a group of risk analysts and data scientists is a plus. 
• Strong risk related domain knowledge, such as credit bureau attributes and scoring, customer acquisition, scorecard modeling, loan amount assignment, pricing, loss forecasting, or collections
• Tracking records of building new lending related products with data driven solutions. 
• Strong business acumen with the ability to build relationships, influence cross functional partners and drive winning results. 
• Proven analytical thinker that can leverage excellent communication skills to translate complex quantitative and qualitative data into actionable insights
• MS/PhD in quantitative fields such as Statistics, Operational Research, Industry Engineering, Economics etc.

Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position may be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at Intuit®: Careers | Benefits). Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender.