Intuit
Staff Fraud AI Scientist - Fintech Consumer Risk Fraud
Intuit, Mountain View, California, us, 94039
Staff Fraud AI Scientist - Fintech Consumer Risk Fraud at Intuit
Join us to apply for this strategic role where you’ll shape the future of risk and fraud management at a leading fintech platform.
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 Data 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
Contribute to the fraud risk AI science initiatives for new and evolving Money product offerings, owning the full model lifecycle, driving the data strategy, and delivering program-level results.
Design, build, deploy, evaluate, defend, and monitor machine learning models to predict and detect fraud risk for our primary banking product (CK Money) and short-term lending products (e.g., tax refund advances, FNPL, installment loans, single payment loans, early wage access).
Collaborate with credit policy, product, and fraud risk teams to ensure models align with business goals and enable actionable lending decisions.
Build efficient and reusable data pipelines for feature generation, model development, scoring, and reporting using Python, SQL, and both commercial and proprietary ML infrastructures.
Deploy models in production environments in collaboration with other AI scientists and ML engineers.
Ensure model fairness, interpretability, and compliance with applicable regulations.
Contribute to the evolution of our data and ML infrastructure within the Intuit ecosystem to increase 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 that give customers (not fraudsters) access to their hard‑earned money.
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 for immediate, substantial, and sustainable impact.
Minimum Qualifications
Advanced Degree (Ph.D. / MS) in Computer Science, Data Science, AI, Mathematics, Statistics, Physics, or a related quantitative discipline.
7‑10 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 experience in credit risk and/or financial fraud risk, with deep understanding of payment systems, money movement products, banking, and lending.
Experience with developing, deploying, monitoring, and maintaining a variety of machine learning techniques, including 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 frameworks 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.
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.
Compensation and Benefits 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. Pay is based on factors such as job‑related knowledge, skills, experience, and work location. The expected base pay range for this position is $250,000.00–$275,000.00.
Organization Details Seniority level : Mid‑Senior level Employment type : Full‑time Job function : Engineering and Information Technology
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Join us to apply for this strategic role where you’ll shape the future of risk and fraud management at a leading fintech platform.
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 Data 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
Contribute to the fraud risk AI science initiatives for new and evolving Money product offerings, owning the full model lifecycle, driving the data strategy, and delivering program-level results.
Design, build, deploy, evaluate, defend, and monitor machine learning models to predict and detect fraud risk for our primary banking product (CK Money) and short-term lending products (e.g., tax refund advances, FNPL, installment loans, single payment loans, early wage access).
Collaborate with credit policy, product, and fraud risk teams to ensure models align with business goals and enable actionable lending decisions.
Build efficient and reusable data pipelines for feature generation, model development, scoring, and reporting using Python, SQL, and both commercial and proprietary ML infrastructures.
Deploy models in production environments in collaboration with other AI scientists and ML engineers.
Ensure model fairness, interpretability, and compliance with applicable regulations.
Contribute to the evolution of our data and ML infrastructure within the Intuit ecosystem to increase 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 that give customers (not fraudsters) access to their hard‑earned money.
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 for immediate, substantial, and sustainable impact.
Minimum Qualifications
Advanced Degree (Ph.D. / MS) in Computer Science, Data Science, AI, Mathematics, Statistics, Physics, or a related quantitative discipline.
7‑10 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 experience in credit risk and/or financial fraud risk, with deep understanding of payment systems, money movement products, banking, and lending.
Experience with developing, deploying, monitoring, and maintaining a variety of machine learning techniques, including 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 frameworks 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.
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.
Compensation and Benefits 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. Pay is based on factors such as job‑related knowledge, skills, experience, and work location. The expected base pay range for this position is $250,000.00–$275,000.00.
Organization Details Seniority level : Mid‑Senior level Employment type : Full‑time Job function : Engineering and Information Technology
#J-18808-Ljbffr