The QuickBooks Capital team is on a mission to transform small business lending. Imagine a world where QuickBooks customers can access funding in hours whenever they need it. And then imagine a world where we help small businesses make the right decision about when to take funding, how much to take, and what type. That’s the future state we’re working to make a reality. QuickBooks is better positioned to solve this customer pain point than any other player in the market and we are assembling a world class team to build this business.
If you are passionate about innovation, solving tough customer challenges, and building a start-up from the ground up, then come join our team!
- This role teams up with our credit strategy and data engineering teams closely to develop innovative credit models for our QuickBooks Financing products.
- Use big data technology to mine massive scale and unstructured data to formulate innovative solutions to build new features and credit models
- Build credit related models on customer segmentations, underwriting decisions and pricing by leveraging statistical algorithms (such as clustering time series analysis) and exploring cutting edge machine learning methods and tools (such as TensorFlow)
- Develop solutions to improve data quality and usability, provide analysis on different level of data aggregations, and make judgmental decision on data structure and feature generation process
- Design, implement, and monitor testing to explore new methodologies for our credit strategies
- Dive deep into our lending portfolio performance and consolidate insights to present in front of business audience and senior/executive leaders
- Work cross functionally with other business partners (product development, marketing, data engineering, etc.) to define data requirements and business needs
- Actively contribute creative ideas to design new financing products for our QuickBooks customers
- MS/PhD in quantitative fields such as Computer Science, Statistics, Operational Research, Industry Engineering, Economics etc.
- Extensive experience in one or more of the following: SQL/relational database, big data technology (e. g., HDFS, Hive, or Spark). Knowing non-relational database or AWS is a plus
- Strong statistical and machine learning skills with at least one of the programing tools (e. g, Python, R or other similar ones)
- Have strong sense and direct experience on processing big data systematically and QA work on the final output
- Having risk related domain knowledge is a plus, such as credit bureau attributes and scoring, scorecard modeling, pricing, loss forecasting, collections and fraud detections
- Excellent communication skills and ability to learn fast
- Confidence in taking ownership and passion in driving changes in a fast-paced working environment
Imagine a career where your creative inspiration can fuel BIG innovation. Year-over-year, Intuit has been recognized as a best employer and is consistently ranked on Fortune's "100 Best Companies To Work For" and Fortune World's "Most Admired Software Companies" lists. Immerse yourself in our award winning culture while creating breakthrough solutions that simplify the lives of consumers and small businesses and their customers worldwide.
Intuit is expanding its social, mobile, and global footprint with a full suite of products and services that are revolutionizing the industry. Utilizing design for delight and lean startup methodologies, our entrepreneurial employees have brought more than 250 innovations to market – from QuickBooks® and TurboTax®, to GoPayment, Mint.com, big data, cloud (SaaS, PaaS) and mobile apps. The breadth and depth of these customer-driven innovations mean limitless opportunities for you to turn your ingenious ideas into reality at Intuit.
Discover what it's like to be part of a team that rewards taking risks and trying new things. It's time to love what you do! Check out all of our career opportunities at: careers.intuit.com. EOE AA M/F/Vet/Disability
Intuit will consider for employment qualified applicants with criminal histories in a manner consistent with requirements of local law.