Job Details

Senior AI Scientist - Consumer Risk Fraud

Join Intuit Credit Karma to develop cutting‑edge credit risk AI/ML models for new lending products. Work with AI scientists, machine learning engineers, product, policy, and fraud risk teams to drive evidence‑based lending decisions impacting hundreds of thousands of customers.

What you'll do:

• Own the end‑to‑end model lifecycle for short‑term lending products (tax refund advances, BNPL, installment loans, single payment loans, early wage access).
• Design, build, deploy, evaluate, defend, and monitor machine learning models to predict credit risk.
• Collaborate with credit policy, product, and fraud risk teams to align models with business goals and product offering.
• 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 environment in collaboration with AI scientists and ML engineers.
• Ensure model fairness, interpretability, and compliance with FCRA, ECOA, and other regulatory frameworks.
• Contribute to evolution of data and ML 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 a fast‑paced, growing environment.

Benefits:

• Competitive base salary in San Diego, CA: $160,500 – $217,000.
• Cash bonus, equity rewards, and comprehensive benefits package.
• Opportunities for professional growth and mentorship.
• Inclusive, collaborative and innovative work environment.

Minimum Basic Requirements:

• Advanced degree (Ph.D. / MS) in Computer Science, Data Science, AI, Mathematics, Statistics, Physics, or related discipline.
• 6+ years of experience in AI Science / Machine Learning and related areas.
• Proficient in Python and SQL.
• Deep experience in fintech credit risk with understanding of payment systems, money movement products, banking, and lending.
• Experience leveraging credit bureau, tax, and cash flow data in credit risk model development.
• Strong background in developing, deploying, monitoring, and maintaining machine learning techniques (deep learning, tree‑based models, reinforcement learning, clustering, time series, causal analysis, natural language processing).
• Deep understanding of credit risk modeling concepts (PD calibration, reject inference, adverse action logic, risk segmentation).
• Ability to develop deep statistical understanding of large, complex datasets.
• Expertise in designing and building efficient and reusable data pipelines and frameworks for ML models.
• Strong business problem solving, communication, and collaboration skills.
• Ambitious, results‑oriented, team player, innovative 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 external technical visibility and innovation agenda.

Preferred Qualifications:

• Proficiency in deep learning ML frameworks such as TensorFlow, PyTorch, etc.
• Experience with public cloud platforms (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.

Equal Employment Opportunity:

Credit Karma is a proud Equal Employment Opportunity Employer. We welcome all candidates without regard to race, color, religion, age, marital status, sex (including pregnancy, childbirth, or related medical condition), sexual orientation, gender identity or expression, national origin, veteran or military status, disability (physical or mental), genetic information or other protected characteristic. We prohibit discrimination of any kind and operate in compliance with applicable fair chance laws.

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