Manager 2, AI Science

TL;DR

Manager 2, AI Science (Credit and Fraud Risk AI/ML): Leading the development, deployment, and monitoring of credit risk and fraud risk models for consumer lending, banking, workforce wallet, and investment products with an accent on model strategy, data governance, and regulatory compliance. Focus on building AI agents and orchestration workflows for the model lifecycle, solving complex risk modeling problems, and integrating real-time and batch decisioning systems.

Location: Mountain View, California, United States

Salary: $237,500–$321,500 base pay annually, with potential bonus, equity rewards, and benefits.

Company

hirify.global is a financial technology platform developing products such as TurboTax, Credit Karma, QuickBooks, and Mailchimp for customers worldwide.

What you will do

• Lead, coach, and grow a team of AI scientists while setting technical direction and delivery standards.
• Own credit risk modeling for consumer lending and fast-money products, including underwriting, behavioral, targeting, and eligibility models.
• Lead fraud modeling across Banking, Workforce Wallet, and CK Invest, integrating models into real-time and batch decisioning throughout the fraud lifecycle.
• Direct data strategy using credit bureau, tax, cashflow, identity, device, and behavioral data to develop proprietary risk attributes and models.
• Own the end-to-end model lifecycle across AWS SageMaker, Redshift, Databricks, and GCP Vertex AI.
• Partner with product, engineering, policy, legal, compliance, validation, and banking stakeholders while communicating roadmap and impact to senior leadership.

Requirements

• Advanced degree in computer science, data science, AI, mathematics, statistics, econometrics, physics, or a related quantitative discipline, or equivalent experience.
• 8+ years of AI Science or machine learning experience, including 2+ years leading or managing AI science or data science teams.
• Authoritative knowledge of Python and SQL.
• Relevant fintech experience in credit risk or fraud risk modeling, with a strong understanding of payment systems, money movement, banking, and lending.
• Hands-on experience developing, deploying, monitoring, and maintaining machine learning models, including deep learning, tree-based models, reinforcement learning, clustering, time series, causal analysis, and natural language processing.
• Experience using credit bureau, tax, cashflow, identity, device, and behavioral data for risk model development.

Nice to have

• Experience with transformers and sequence modeling.
• Experience designing AI agents and orchestration workflows for machine learning operations.

Culture & Benefits

• Work on high-impact problems involving customer protection and access to credit.
• Collaborate with executives and cross-functional teams across engineering, product, policy, analytics, operations, and AI science.
• Support professional growth and knowledge sharing across the organization.
• Potential eligibility for cash bonus, equity rewards, and employee benefits.
• Participation in fair-pay reviews across ethnicity and gender categories.