Listed on Intuit’s own careers site. You apply with them directly — we never stand between you and the employer.
What this role is
Intuit is hiring a Staff AI Scientist to build credit risk models for their Consumer Lending products, including tax refund advances and buy-now-pay-later offerings. This role suits experienced ML practitioners who want to own the full model lifecycle in fintech and work on high-impact lending decisions affecting hundreds of thousands of customers.
Our summary, not Intuit’s wording. The full posting is on their site.
Skills this role names
- Amazon Web Services (AWS)
- Apache Airflow
- AWS SageMaker
- Deep Learning
- LangChain
- LangGraph
- LLMs (Large Language Models)
- Natural Language Processing
- Prompt Engineering
- Python
- PyTorch
- Reinforcement Learning
- Retrieval-Augmented Generation (RAG)
- SQL
- TensorFlow
- Time Series Analysis
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What they ask for
Required
- Advanced degree (Ph.D. or Master's) in Computer Science, Data Science, AI, Mathematics, Statistics, Physics, or related quantitative field
- 4+ years of work experience in AI science or machine learning
- Expert-level proficiency in Python and SQL
- Work experience in fintech credit risk with understanding of payment systems, banking, and lending products
- Experience developing credit risk models using credit bureau, tax, and cash flow data
- Hands-on expertise building, deploying, and maintaining deep learning, tree-based, reinforcement learning, clustering, time series, causal analysis, and NLP models
- Deep understanding of credit risk concepts including PD calibration, reject inference, adverse action logic, and risk segmentation
- Ability to quickly develop statistical understanding of large complex datasets
- Expertise designing efficient, reusable data pipelines for machine learning
- Strong business problem-solving, communication, and collaboration skills
- Proven experience defining end-to-end modeling frameworks and methodologies across teams or domains
- Demonstrated ability to evaluate and integrate emerging AI/ML technologies
Nice to have
- Proficiency in TensorFlow, PyTorch, or similar deep learning frameworks
- Experience with public cloud platforms (GCP or AWS) and workflow orchestration tools like Apache Airflow
- Strong background in MLOps infrastructure (Vertex AI or AWS SageMaker) including pipelines, automated retraining, monitoring, and version control
- Experience with experimentation design, A/B testing, and statistical analysis
- Knowledge of LLMs, AI agents, prompt engineering, RAG, and tool calling
- Familiarity with orchestration frameworks like LangChain or LangGraph and the Gen AI stack
- Experience building transaction categorization and cash flow modeling pipelines from bank data sources like Plaid, Nova Credit, MX, or Finicity