Data Scientist (Consumer Credit Risk)
QuadPay is an alternative payment provider that allows brands to give their customers the opportunity to split their purchases into 4 interest-free, automatic installments. The customer gets the product straight away and we pay the merchant upfront.
QuadPay is like lay-away for the online generation. We're one of the fastest-growing payments startups in New York City and are looking for a Data Scientist to join our team.
Underpinning QuadPay is a risk and decisioning process that instantaneously approves or rejects users when they are checking out from our merchant partners. In this role, you’ll be focused on developing, improving and optimizing our real-time decisioning tools utilizing various dimensions and data points to determine a customers propensity to repay their installments with the aim of reducing overall risk and default rates.
We are looking for a Data Scientist with consumer credit risk experience to join the QuadPay Risk team to identify trends in our data and to develop risk models and real-time decisioning algorithms utilizing many data points received during the eCommerce transaction process. You will have the opportunity to solve problems across large data sets using internal and external data sources. You’ll have exposure to all aspects of modelling and will have the chance to define and engineer solutions that will underpin millions of transactions per year.
Things you will do
- Build models and predictive tools that assist QuadPay in making smart decisions at the point of transaction with respect to new and existing customers who want to transact
- Build algorithms and models designed to minimize loss rate while maximising approval rates across our merchant base
- Regression test algorithmic changes and push successful tests into production then test, measure and iterate
- Critically interrogate data and patterns then compiles learnings into insights and turn these insights into features
- Find new and useful data sources and build into our models
- Maintain data models, set and benchmark against performance thresholds and monitor performance of our decisioning in real-time
- 2- 4 years experience performing quantitative analysis within self-directed roles. Experience with credit/fraud risk modeling is preferred.
- Degree in Statistics, Applied Mathematics, Engineering, Computer Science or other quantitative fields from leading university; Advanced degree preferred
- Practical hands-on experience in the development and implementation of new predictive models and comfortable in learning new statistical tools and techniques.
- Ability to visualize and communicate sophisticated data and models to all audiences.
- Specific demonstrated experience, knowledge, accuracy and speed of execution in Powerpoint, Excel, Excel VBA and R (knowledge of other programming knowledge and/or Stata is a plus)
- Highly motivated, versatile, capable of working independently with demonstrated initiative
- Hands-on experience and familiarity with machine-learning techniques, statistics, and optimization
- Ability to derive insights that will have positive impact and the ability to turn these into production ready solutions
- Native in R, Python or similar and SQL is a necessary
- Competitive Salary
- Employee Options Scheme, which means all employees have a meaningful stake in the business
- Generous leave entitlements
- Generous staff referral program