Intuit's Consumer Group is expanding its focus to Consumer Lending and requires data scientists to build AI/ML models for consumer lending. This role will be crucial in supporting these initiatives while navigating the complexities of Fintech Risk.
Requirements
- Ability to tell stories with data, educate effectively, and instill confidence, motivating stakeholders to act on recommendations.
- Proven ability to apply scientific methods to solve real-world problems on web-scale data.
- Strong business and product sense: delight in shaping vague questions into well-defined analyses and success metrics that drive business decisions.
- Advanced proficiency in SQL, Tableau, and Excel, with expertise in modern advanced analytical tools and programming languages such as Python.
- Experience in defining metrics and instrumenting data tracking in clickstream.
- Excellent problem-solving skills and end-to-end quantitative thinking.
- Ability to manage multiple projects simultaneously to meet objectives and key deadlines.
Responsibilities
- Perform hands-on data analysis and modeling to derive meaningful conclusions and solve complex problems of lending.
- Extract insights and knowledge from structured and unstructured data using various techniques, including statistical analysis, machine learning, and data visualization in service to driving business decisions.
- Run regular A/B tests, gather data, perform statistical analysis, draw conclusions on the impact of product experiences, and communicate results to peers and leaders to measure and optimize execution of data-driven strategies in emerging areas of the consumer lending roadmap.
- Utilize models and develop advanced experimentation methods, such as synthetic controls, propensity score matching techniques, etc., to establish causality and measure product performance.
- Partner closely with credit and fraud risk policy teams in deploying models and inferring results by monitoring the performance and effectiveness of strategies.
Other
- A great storyteller and communicator who can build relationships with a diverse set of stakeholders, including both technical and non-technical colleagues.
- MS in Statistics, Mathematics, Computer Science, Economics, Operations Research, or equivalent work experience is preferred.
