Staff Credit AI Scientist (Fintech)

TL;DR

Staff Credit AI Scientist (Fintech): Develops cutting-edge credit risk AI/ML models for new lending products, focusing on the lending domain, including complete hands-on ownership of the model lifecycle. Focus on designing and deploying machine learning models to predict credit risk for short-term lending products and contributing to the evolution of data and machine learning infrastructure.

Location: Oakland, Charlotte, Culver City, San Diego, London, Bangalore, and New York City.

Salary: $205,500 - 278,000

Company

hirify.global empowers millions of individuals to take control of their finances through TurboTax and Credit Karma.

What you will do

• Design, build, deploy, evaluate, and monitor machine learning models to predict credit risk for lending products.
• Collaborate with credit policy, product, and fraud risk teams to ensure models align with business goals and product offerings.
• Build efficient and reusable data pipelines for feature generation, model development, scoring, and reporting using Python and SQL.
• Deploy models in a production environment in collaboration with AI scientists and machine learning engineers.
• Ensure model fairness, interpretability, and compliance with regulatory frameworks.
• Contribute to the evolution of data and machine learning infrastructure to improve the efficiency and effectiveness of AI science solutions.

Requirements

• Advanced Degree (Ph.D. / MS) in Computer Science, Data Science, AI, Mathematics, Statistics, Physics, or a related quantitative discipline.
• 6+ 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 understanding of credit risk modeling concepts, including PD calibration, reject inference, adverse action logic, and risk segmentation.

Nice to have

• 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.

Culture & Benefits

• Solve hard, meaningful problems, giving customers access to their hard-earned money alongside fun, smart people.
• Experience professional growth and encourage growth throughout the team.
• Work cross-functionally to ensure the efficient and effective use of data science.