Intuit is looking to solve the problem of credit risk assessment for its new lending products, including tax refund advances, BNPL, installment loans, single payment loans, and early wage access, by developing cutting-edge credit risk AI/ML models.
Requirements
- Authoritative knowledge of Python and SQL
- Relevant work experience in fintech credit risk, with deep understanding of payment systems, money movement products, banking, and lending
- Experience leveraging credit bureau, tax and cash flow data in credit risk model development
- Experience with and deep understanding of developing, deploying, monitoring and maintaining a variety of machine learning techniques, including but not limited to, deep learning, tree-based models, reinforcement learning, clustering, time series, causal analysis, and natural language processing.
- Deep understanding of credit risk modeling concepts, including PD calibration, reject inference, adverse action logic, and risk segmentation
- Expertise in designing and building efficient and reusable data pipelines and framework for machine learning models
- Proficiency in deep learning ML frameworks such as TensorFlow, PyTorch, etc.
Responsibilities
- Contribute to the credit risk AI science initiatives for the new and evolving Money product offerings focusing on the lending domain, including complete hands-on ownership of the model lifecycle, sharing ownership of success and key results at the program-level, and driving the data strategy across all involved teams.
- Design, build, deploy, evaluate, defend, and monitor machine learning models to predict credit risk for various short-term lending products (e.g., tax refund advances, BNPL, installment loans, single payment loans, and early wage access)
- Collaborate with credit policy, product and fraud risk teams to ensure models align with business goals and product offering to drive actionable lending decisions
- Build efficient and reusable data pipelines for feature generation, model development, scoring, and reporting using Python, SQL, and both commercially available and proprietary Machine Learning and AI infrastructures
- Deploy models in a production environment in collaboration with other AI scientists and machine learning engineers
- Ensure model fairness, interpretability, and compliance with FCRA, ECOA, and other relevant regulatory frameworks
- Contribute to the evolution of our data and machine learning infrastructure within the Intuit ecosystem to improve efficiency and effectiveness of AI science solutions.
Other
- Advanced Degree (Ph.D. / MS) in Computer Science, Data Science, AI, Mathematics, Statistics, Physics or a related quantitative discipline
- 3-7 years of work experience in AI Science / Machine Learning and related areas
- Strong business problem solving, communication and collaboration skills
- Ambitious, results oriented, hardworking, team player, innovator and creative thinker
- Ability to quickly develop a deep statistical understanding of large, complex datasets
