Intuit
Senior Fraud AI Scientist - Consumer Risk Fraud
Intuit, San Diego, California, United States, 92189
Senior Fraud AI Scientist - Consumer Risk Fraud
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Senior Fraud AI Scientist – Consumer Risk Fraud
role at
Intuit .
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.
Intuit’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 and evolve our consumer offerings to deliver even greater value.
Responsibilities
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 program‑level success and key results, and driving data 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 engineers.
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.
Qualifications
Advanced Degree (Ph.D. / M.S.) in Computer Science, Data Science, AI, Mathematics, Statistics, Physics or a related quantitative discipline.
3–6 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 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.
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.
Compensation Base pay range:
Bay Area, CA: $173,500 - $234,500
Southern California, CA: $160,500 - $217,000
New York: $172,000 - $232,500
Seniority Level Mid‑Senior level
Employment Type Full‑time
Job Function Engineering and Information Technology
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Senior Fraud AI Scientist – Consumer Risk Fraud
role at
Intuit .
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.
Intuit’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 and evolve our consumer offerings to deliver even greater value.
Responsibilities
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 program‑level success and key results, and driving data 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 engineers.
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.
Qualifications
Advanced Degree (Ph.D. / M.S.) in Computer Science, Data Science, AI, Mathematics, Statistics, Physics or a related quantitative discipline.
3–6 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 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.
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.
Compensation Base pay range:
Bay Area, CA: $173,500 - $234,500
Southern California, CA: $160,500 - $217,000
New York: $172,000 - $232,500
Seniority Level Mid‑Senior level
Employment Type Full‑time
Job Function Engineering and Information Technology
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