Senior Staff AI Scientist - Consumer Risk

Qualifications

• Minimum Requirements
• Advanced degree (Ph.D. / MS) in Computer Science, Data Science, AI, Mathematics, Statistics, Econometrics, Physics, or a related quantitative discipline (or equivalent experience).
• 8+ years of experience in AI Science / Machine Learning, successfully delivering data-driven products.
• Authoritative knowledge of Python and SQL.
• Relevant fintech experience in credit risk and/or fraud risk modeling, with a deep understanding of payment systems, money movement, banking, and lending.
• Hands-on expertise developing, deploying, monitoring and maintaining a variety of machine learning techniques, including but not limited to, deep learning (transformers, sequence modeling), tree-based models, reinforcement learning, clustering, time series, causal analysis, and natural language processing.
• Experience leveraging credit bureau, tax, cashflow, identity, and device/behavioral data in risk model development.
• Deep understanding of credit risk concepts (PD calibration, reject inference, adverse action logic, risk segmentation) and/or fraud typologies (onboarding/synthetic ID, ACH/deposit fraud, transaction / debit auth fraud, ATO, fraud rings).
• Expertise designing efficient, reusable data pipelines and frameworks for ML models.
• Strong business problem-solving, communication, collaboration, and people-leadership skills; able to communicate a vision and inspire others to innovate.
• Preferred Qualifications Proficiency in deep learning frameworks such as TensorFlow or PyTorch. Experience with public cloud platforms (GCP or AWS) and workflow orchestration tools such as Apache Airflow. Strong background in MLOps infrastructure and tooling — particularly Vertex AI or AWS SageMaker — including pipelines, automated retraining, monitoring, and version control. Experience with real-time / streaming model deployment and feature stores for low-latency fraud decisioning. Working knowledge of LLMs and AI agents (prompt engineering, RAG, tool calling, agentic workflows) and Gen AI orchestration frameworks (e.g., LangChain, LangGraph). Experience with model governance and regulatory frameworks (SR 11-7, FCRA, ECOA) and fair lending / fair banking review.
• Proficiency in deep learning frameworks such as TensorFlow or PyTorch.
• Experience with public cloud platforms (GCP or AWS) and workflow orchestration tools such as Apache Airflow.
• Strong background in MLOps infrastructure and tooling — particularly Vertex AI or AWS SageMaker — including pipelines, automated retraining, monitoring, and version control.
• Experience with real-time / streaming model deployment and feature stores for low-latency fraud decisioning.
• Working knowledge of LLMs and AI agents (prompt engineering, RAG, tool calling, agentic workflows) and Gen AI orchestration frameworks (e.g., LangChain, LangGraph).
• Experience with model governance and regulatory frameworks (SR 11-7, FCRA, ECOA) and fair lending / fair banking review.
• Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position may be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at Intuit®: Careers | Benefits ). Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender.