Senior Staff AI Scientist - Consumer Risk

Company OverviewIntuit is the global financial technology platform that powers prosperity for the people and communities we serve. With tens of millions of customers worldwide using products such as TurboTax, Credit Karma, QuickBooks, and Mailchimp, we believe that everyone should have the opportunity to prosper. We never stop working to find new, innovative ways to make that possible.Job OverviewIntuit’s Consumer Group, including TurboTax and Credit Karma, empowers millions of individuals to take control of their finances. By harnessing the power of data and artificial intelligence (AI), we continuously innovate across consumer lending, banking, and money-movement products to deliver greater value and to protect our members and our platform from risk.As we expand our Consumer Risk AI Science charter, Intuit Credit Karma is looking for an experienced, hands-on player-coach to join us as a Sr Staff, AI Science. In this role you will build, deploy, and monitor credit risk and fraud risk AI/ML models that directly affect hundreds of thousands of customers — while staying close enough to the technical work to set a high bar, unblock the team, and personally drive the most complex, ambiguous modeling problems. You will own credit risk modeling for our consumer lending and fast-money products, and you will lead the expansion of our fraud modeling program — across three business verticals: Banking, Workforce Wallet (WFS), and CK Invest. You will partner with the broader Consumer Risk leadership team to set strategy, drive delivery, and develop talent.ResponsibilitiesTeam & Delivery LeadershipLead, and build AI/ML models Set the modeling strategy — owning accountability for the design, development, deployment, and monitoring of credit risk and fraud risk models; set technical direction, review the team’s work, remove blockers, and share ownership of program-level success and key results.Direct the team’s data strategy — governing the sourcing and use of internal and third-party data such as credit bureau (Experian, TransUnion, Lexis Nexis), cashflow transaction (Plaid, Nova Credit), identity and fraud (Socure, Emailage, ID Analytics) and Intuit tax data — to build proprietary risk attributes and models.Own credit risk AI science for the consumer lending and fast-money portfolio — including first-generation and next-generation credit risk underwriting, cashflow-based underwriting, behavioral, and targeting / eligibility models — for short-term lending products (e.g., tax refund advances, BNPL, installment loans, line of credit, and early wage access). Lead fraud AI/ML modeling across three verticals — Banking, Workforce Wallet (WFS), and CK Invest — owning the fraud model roadmap and its integration into real-time and batch decisioning across the full fraud lifecycle: onboarding, money-in, money-out and account takeover (ATO) fraud.Own the end-to-end model lifecycle — across cloud infrastructure on Intuit AWS (SageMaker, Redshift, Databricks) and CK GCP Vertex AIOwn model governance and regulatory compliance (SR 11-7, FCRA, ECOA), including fair lending reviews, adverse action reason codes, and periodic performance reporting to partner banks and internal governance.Partner with Credit and Fraud Policy, Product, Engineering, Legal & Compliance, Model Validation, and partner banks (WebBank, MVB) to embed models into decisioning and to shape risk solutions at the earliest stages of every product launch.Communicate team progress, model impact, and roadmap to senior Risk and Business leadership through regular updates, deep dives, and executive reviews.Lead hiring for the AI Science organization — conducting interviews, representing the modeling function on cross-team panels, and onboarding new employees — and sponsor knowledge-sharing sessions and adoption of modern AI tooling to increase team velocity and productivity.Champion a culture of Agentic AI adoption while guiding the team to design, build, and deploy AI agents and orchestration workflows that automate the end-to-end model development lifecycle—data exploration, feature engineering, validation, training, evaluation, and monitoring—to accelerate team velocity and productivity.QualificationsMinimum RequirementsAdvanced 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 QualificationsProficiency 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. The expected base pay range for this position is: Mountain View $226,000 - $306,000