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OverviewCompany 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.

Job Description 

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 into Consumer Lending within the Consumer Group, 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 models for new lending 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 credit risk AI science initiatives for the new and evolving Money product offerings focusing on the lending domain, including complete hands-on ownership of the model lifecycle, sharing ownership of success and key results at the program-level, and driving the data  strategy across all involved teams.

  • Design, build, deploy, evaluate, defend, and monitor machine learning models to predict credit risk for various short-term lending products (e.g., tax refund advances, BNPL, installment loans, line of credit, 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 with FCRA, ECOA, and other relevant regulatory frameworks 
    

• Build next-generation credit risk models for short-term lending products using advanced deep learning techniques (e.g., transformers, sequence models, and representation/embedding learning on tabular and time-series financial data) 

• Build and improve transaction categorization models that power cash flow underwriting and credit risk models for thin-file and sub-prime consumers.

• 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.

• Design, build, and deploy AI agents and orchestration workflows powered by Agentic AI to automate the end-to-end model development lifecycle—data exploration, feature engineering, data validation, model training, evaluation, and monitoring—accelerating team velocity and productivity.

Qualifications

Minimum Basic Requirements:

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

• 4+ years of work experience in AI Science / Machine Learning and related areas

• Authoritative knowledge of Python and SQL

• Relevant work experience in fintech credit risk, with deep understanding of payment systems, money movement products, banking, and  lending

• Experience leveraging credit bureau, tax and cash flow data in credit risk model development

• Deep, hands-on expertise developing, deploying, monitoring and maintaining a variety of machine learning techniques, including but not limited to, deep learning (transformers, sequence modeling), tree-based models, reinforcement learning, clustering, time series, causal analysis, and natural language processing.  

• Deep understanding of credit risk modeling concepts, including PD calibration, reject inference, adverse action logic, and risk segmentation

• 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.

• Working knowledge of LLMs and AI agents (prompt engineering, RAG, tool calling, agentic workflows) and familiarity with orchestration frameworks (e.g., LangChain, LangGraph) and the Gen AI stack (embeddings, vector databases, fine-tuning).

• Experience building transaction categorization and cash flow modeling pipelines from bank/aggregator data (e.g., Plaid, Nova Credit, MX, Finicity) for credit risk or underwriting use cases.

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Intuit provides a competitive compensation p