Intuit
Staff Fraud AI Scientist - Fintech Consumer Risk Fraud
Intuit is a global financial technology platform that empowers one billion people worldwide to prosper through products such as TurboTax, Credit Karma, QuickBooks, and Mailchimp. At Intuit, we continuously innovate with data and artificial intelligence (AI) to deliver greater value to our customers. Our Consumer Group focuses on products that give individuals control over their finances, and as we expand our primary banking and lending offerings, we need a skilled AI scientist to develop cutting‑edge credit‑risk and fraud‑prediction models.
Responsibilities
Own the fraud‑risk AI initiatives for new and evolving money‑product offerings, driving end‑to‑end model lifecycle and program‑level success.
Design, build, deploy, evaluate, defend, and monitor machine‑learning models that predict and detect fraud risk for 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 align models with business goals and enable actionable lending decisions.
Build efficient, reusable data pipelines for feature generation, model development, scoring, and reporting with Python, SQL, and proprietary/enabled AI infrastructures.
Deploy models into production in partnership with AI scientists and engineering teams.
Maintain model fairness, interpretability, and compliance with regulatory requirements.
Advance Intuit’s data and ML infrastructure to improve overall AI‑science efficiency.
Research and implement creative ML and statistical methods suitable for a fast‑paced, growth‑oriented environment.
Minimum Qualifications
Advanced degree (Ph.D. or MS) in Computer Science, Data Science, AI, Mathematics, Statistics, Physics, or a related quantitative field.
7‑10 years of experience in AI science, machine learning, or related areas.
Expertise in Python and SQL.
Deep experience in fintech fraud risk and money‑movement products (banking, lending, fraud detection).
Experience in credit risk and/or financial fraud risk with payment systems and money‑movement products.
Proficiency in developing, deploying, monitoring, and maintaining diverse ML techniques (deep learning, tree‑based models, reinforcement learning, clustering, time‑series, causal analysis, NLP).
Comprehensive understanding of fraud‑risk modeling concepts (score calibration, label bias correction, case disposition logic, network/graph‑based link analysis).
Ability to work with large, complex data sets and provide deep statistical insights.
Expertise in designing efficient data pipelines and ML frameworks.
Strong business problem‑solving, communication, and collaboration skills.
Ambitious, results‑oriented, team‑player, innovative, and creative thinker.
Demonstrated experience defining and driving end‑to‑end modeling frameworks across multiple product teams.
Ability to evaluate and integrate emerging AI/ML technologies.
Preferred Qualifications
Proficiency with deep‑learning frameworks such as TensorFlow, PyTorch, or equivalents.
Experience on public cloud platforms (GCP or AWS) and workflow orchestration tools like Apache Airflow.
Background in MLOps infrastructure and tooling—Vertex AI, SageMaker, pipelines, automated retraining, monitoring, version control.
Experience with experiment design and analysis (A/B testing, statistical analysis).
Benefits (Overview)
Competitive pay for performance with cash bonus, equity rewards, and benefits.
Inclusive workplace with a commitment to equal opportunity and diversity.
Professional growth opportunities across cross‑functional teams.
Impactful work that directly protects customers and enhances their financial well‑being.
Seniority Level Mid‑Senior level
Employment Type Full‑time
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Responsibilities
Own the fraud‑risk AI initiatives for new and evolving money‑product offerings, driving end‑to‑end model lifecycle and program‑level success.
Design, build, deploy, evaluate, defend, and monitor machine‑learning models that predict and detect fraud risk for 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 align models with business goals and enable actionable lending decisions.
Build efficient, reusable data pipelines for feature generation, model development, scoring, and reporting with Python, SQL, and proprietary/enabled AI infrastructures.
Deploy models into production in partnership with AI scientists and engineering teams.
Maintain model fairness, interpretability, and compliance with regulatory requirements.
Advance Intuit’s data and ML infrastructure to improve overall AI‑science efficiency.
Research and implement creative ML and statistical methods suitable for a fast‑paced, growth‑oriented environment.
Minimum Qualifications
Advanced degree (Ph.D. or MS) in Computer Science, Data Science, AI, Mathematics, Statistics, Physics, or a related quantitative field.
7‑10 years of experience in AI science, machine learning, or related areas.
Expertise in Python and SQL.
Deep experience in fintech fraud risk and money‑movement products (banking, lending, fraud detection).
Experience in credit risk and/or financial fraud risk with payment systems and money‑movement products.
Proficiency in developing, deploying, monitoring, and maintaining diverse ML techniques (deep learning, tree‑based models, reinforcement learning, clustering, time‑series, causal analysis, NLP).
Comprehensive understanding of fraud‑risk modeling concepts (score calibration, label bias correction, case disposition logic, network/graph‑based link analysis).
Ability to work with large, complex data sets and provide deep statistical insights.
Expertise in designing efficient data pipelines and ML frameworks.
Strong business problem‑solving, communication, and collaboration skills.
Ambitious, results‑oriented, team‑player, innovative, and creative thinker.
Demonstrated experience defining and driving end‑to‑end modeling frameworks across multiple product teams.
Ability to evaluate and integrate emerging AI/ML technologies.
Preferred Qualifications
Proficiency with deep‑learning frameworks such as TensorFlow, PyTorch, or equivalents.
Experience on public cloud platforms (GCP or AWS) and workflow orchestration tools like Apache Airflow.
Background in MLOps infrastructure and tooling—Vertex AI, SageMaker, pipelines, automated retraining, monitoring, version control.
Experience with experiment design and analysis (A/B testing, statistical analysis).
Benefits (Overview)
Competitive pay for performance with cash bonus, equity rewards, and benefits.
Inclusive workplace with a commitment to equal opportunity and diversity.
Professional growth opportunities across cross‑functional teams.
Impactful work that directly protects customers and enhances their financial well‑being.
Seniority Level Mid‑Senior level
Employment Type Full‑time
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