Principal AI Scientist, Fintech Innovation Architecture

Overview

Join an amazing team driving cutting-edge AI solutions in the fintech industry, primarily focused on evaluating risk for lending products. This Principal role requires an exceptional blend of deep research, architectural mastery, and a powerful innovation mindset to pioneer new capabilities and deliver high-quality, end-to-end AI systems that shape the product direction for the next 5–10 years.

Responsibilities

System Architecture & Ownership

• Own projects from conception to production, collaborating globally with partners to deliver complex projects.
• Define the vision and direction for end-to-end solutions, serving as a thought leader on execution that brings immediate value and solves for the long-term vision.
• Deliver robust, high-performance AI/ML system designs that meet critical requirements (latency, throughput, cost, maintenance).
• Drive cross-functional end-to-end design architecture.
  
  

• Lead the full cycle of big data exploration, including hypothesis formulation, advanced predictive statistical modeling, and algorithm development across complex, large-scale data.
• Mentor existing Data Science teams, influence AI-driven product design and strategy with partner architects, and assist in the definition of roles and training needs.
  
  

• Pioneer innovative Proof of Concepts (POCs) to show the way for where Intuit’s Fintech products should go over the next 5–10 years, focusing on high-impact solutions like Explainable AI, Homomorphic Encryption, or advanced Behavioral Analytics.
• Apply research to create high-impact solutions and represent the team’s innovation to the wider organization.
• Act as a Consultative Business Facilitator, exploiting revealed insights and providing entrepreneurial guidance for business stakeholders.
  
  

• Advanced degree (Statistics, Applied Mathematics, Computer Science, or related) or equivalent experience
• Expert-level knowledge with modern AI development frameworks, and expertise in all steps spanning the end-to-end Model Development Life Cycle (MDLC)
• Deep technical understanding of underlying Data Science concepts, going beyond just model training
• Industry expert in ML, NLP, or Optimization
• Expert knowledge in a range of statistical and machine learning techniques, including Supervised, Unsupervised, and Generative AI (autoencoders, GANs, VAEs)
• 5+ years of experience in developing predictive models in a financial services or risk management context is highly valued
• Experience with ETL, data warehousing, cloud platforms, and Spark for ML feature creation
• Strong communication and presentation skills to present complex insights to non-technical stakeholders
• Large corporate experience and know-how, and demonstrable maturity are essential traits