$226,000 – $306,000
Listed on Intuit’s own careers site. You apply with them directly — we never stand between you and the employer.
What this role is
This role leads the design and deployment of credit and fraud risk models for Intuit's consumer lending and banking products, affecting hundreds of thousands of customers. It suits an experienced AI scientist comfortable moving between hands-on modeling work and team leadership, who can navigate regulatory requirements and partner with product and engineering to ship risk solutions at scale.
Our summary, not Intuit’s wording. The full posting is on their site.
Skills this role names
- Apache Airflow
- AWS SageMaker
- Deep Learning
- LangChain
- LangGraph
- Natural Language Processing
- Python
- PyTorch
- Reinforcement Learning
- SQL
- TensorFlow
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What they ask for
Required
- Advanced degree in Computer Science, Data Science, AI, Mathematics, Statistics, Econometrics, Physics, or related quantitative discipline
- 8+ years building and delivering machine learning products
- Expertise in Python and SQL
- Experience in credit risk and/or fraud risk modeling with understanding of payment systems, lending, and banking
- Hands-on development and deployment of deep learning, tree-based, reinforcement learning, clustering, time series, and NLP models
- Experience with credit bureau, tax, cashflow, identity, and behavioral data
- Deep knowledge of credit risk concepts (PD calibration, reject inference, adverse action logic, risk segmentation) and fraud typologies
- Design of efficient data pipelines and ML frameworks
- Strong communication, collaboration, and people-leadership skills
Nice to have
- Proficiency in TensorFlow or PyTorch
- Experience with GCP or AWS public cloud platforms
- Experience with Apache Airflow or workflow orchestration tools
- Strong MLOps infrastructure background with Vertex AI or SageMaker
- Real-time and streaming model deployment experience
- Feature stores for low-latency fraud decisioning
- Knowledge of LLMs, AI agents, and prompt engineering
- Experience with SR 11-7, FCRA, ECOA regulatory frameworks and fair lending review