Staff AI Scientist - Fintech Consumer Fraud Risk

Overview

Intuit is the global financial technology platform that powers prosperity for the people and communities we serve. With approximately 100 million 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.

Intuit’s Consumer Group, including TurboTax and Credit Karma, empowers millions of individuals to take control of their finances. TurboTax simplifies tax preparation and enables our customers to file with confidence. By harnessing the power of data and artificial intelligence (AI), we continuously innovate and evolve our consumer offerings to deliver even greater value.

As we expand our primary banking and lending products, Intuit Credit Karma is looking for an innovative, experienced, and hands-on Staff AI Scientist to join our Consumer Risk AI Science team. In this role, you’ll develop cutting-edge credit risk AI/ML fraud models to enable existing and new money movement products. Join a collaborative and inventive team of AI scientists and machine learning engineers where your work will have a direct impact on hundreds of thousands of customers.

Responsibilities

What you’ll do:

• Contribute to the fraud risk AI science initiatives for the new and evolving Money product offerings, including complete hands-on ownership of the model lifecycle, sharing ownership of success and key results at the program-level, and driving the modeling strategy across all involved teams.

  • Design, build, deploy, evaluate, defend, and monitor machine learning models to predict and detect fraud risk for our primary banking product (CK Money) and various short-term lending products (e.g., tax refund advances, FNPL, 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 enginers
  • Ensure model fairness, interpretability, and compliance

• Contribute to the evolution of our data and machine learning infrastructure within the Intuit ecosystem to improve efficiency and effectiveness of AI science solutions.

• Research and implement practical and creative machine learning and statistical approaches suitable for our fast-paced, growing environment.

What’s great about the role:

• Solve hard, meaningful problems giving customers (not fraudsters) access to their hard-earned money alongside fun, smart people.

• Experience professional growth and encourage growth throughout the team.

• Work cross functionally (with executives, engineering, policy & rules, product, analytics, operations and other AI science teams) to ensure efficient and effective use of data science in ways that make an immediate, substantial, and sustainable impact

Qualifications

Minimum Basic Requirements:

• Advanced Degree (Ph.D. / MS) in Computer Science, Data Science, AI, Mathematics, Statistics, Physics or a related quantitative discipline

• 6+ years of work experience in AI Science / Machine Learning and related areas
• Authoritative knowledge of Python and SQL
• Relevant work experience in fintech fraud risk, with deep understanding of money movement products, banking, lending, and fraud detection data
• Relevant work experience in credit risk and/or financial fraud risk, with deep understanding of payment systems, money movement products, banking, and lending
• 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 fraud risk modeling concepts, including fraud score calibration, label bias correction, case disposition logic, and network or graph-based link analysis for identifying organized or collusive fraud patterns.
• Ability to quickly develop a deep statistical understanding of large, complex datasets
• Expertise in designing and building efficient and reusable data pipelines and framework for machine learning models
• Strong business problem solving, communication and collaboration skills
• Ambitious, results oriented, hardworking, team player, innovator and creative thinker
• Proven experience defining and driving end-to-end modeling frameworks, methodologies, or best practices across multiple product teams or domains.

• Demonstrated ability to evaluate and integrate emerging AI/ML technologies, contributing to the company’s external technical visibility and innovation agenda.

Preferred Qualifications:

• Proficiency in deep learning ML frameworks such as TensorFlow, PyTorch, etc.

• Work experience with public cloud platforms (especially GCP or AWS) and workflow orchestration tools like 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 experimentation design and analysis, including A/B testing and statistical analysis.

Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position will 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: