Lead AI Scientist

FICO; San Diego (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

FICO seeks a Lead AI Scientist to build production-grade AI systems at scale within highly regulated financial environments. You will design, train, and deploy transformer-based models, embeddings, and generative AI to curb risk and fraud for enterprise clients.

You will own end-to-end model lifecycles, establish best practices for data and observability, and collaborate with researchers, engineers, and product specialists to translate ideas into practical, client-ready solutions.

Qualifications

• PhD or Master’s in AI/ML/CS/Statistics/Math with hands-on predictive modeling.
• Proven ability to move from research to production deployment.
• Experience with regulated environments is a plus.

Responsibilities

• Design, train, and evaluate models from traditional to transformer-based and generative AI.
• Lead end-to-end ML lifecycle, including data design, eval, observability, and deployment.
• Drive model innovation and production-ready solutions for enterprise use.
• Collaborate with researchers, engineers, and product teams to deploy models on the FICO platform.
• Engage with clients and pre-sales to demonstrate capabilities and drive adoption.

Skills

Python

Kotlin

Java

C++

PyTorch

TensorFlow

JAX

DeepSpeed

Linux

Education

PhD or Master’s in AI/ML/CS/Statistics/Math

Tools

PyTorch

TensorFlow

JAX

DeepSpeed

Triton

Job description

FICO is looking for a Lead AI Scientist to join a team building production-grade AI systems for some of the world's largest financial institutions. This is a rare opportunity to work at the intersection of cutting-edge AI and one of the highest-stakes, most regulated environments in the industry where your models directly influence credit and fraud decisions affecting millions of people.

What You’ll Contribute

• Design, train, and evaluate advanced models from statistical and ML models to transformer-based models, LLMs, and generative AI to solve real-world problems across risk and fraud domains for enterprise applications at scale.
• Design and develop end-to-end model build lifecycle, establishing best practices for data design, model evaluation, observability, and responsible AI deployment in a highly regulated environment.
• Identify and pursue opportunities to drive model innovation, translating research ideas into production-ready solutions that enhance model performance and business value.
• Collaborate with cross-functional teams of researchers, engineers, and product specialists across FICO to translate cutting-edge ideas into practical solutions from model design and development through to model deployment into FICO platform and go-live client support.
• Engage with clients and pre-sales team to demonstrate model capabilities, support client evaluations, and help drive model adoption by showcasing deep understanding of the market differentiating value of the FICO models.

What We’re Seeking

• PhD or Master's in Artificial Intelligence, Machine Learning, Computer Science, Statistics, Mathematics, or a related technical field, with hands-on experience in predictive modeling, and applied AI development and a demonstrated ability to work independently and drive problems to resolution.
• Deep hands-on understanding of ML models, transformer architectures, embeddings, and attention mechanisms, with the curiosity and critical reasoning to go beyond standard open-source framework usage including model training, fine-tuning, and adaptation to domain-specific problems.
• Demonstrated track record of taking end-to-end ownership of AI/ML solutions from research to production deployment, with open-source or research contributions such as publications, patents considered a strong plus.
• Strong competency in two or more programming languages (Python, Kotlin, Java, C, C++) and deep learning frameworks (PyTorch, TensorFlow, JAX, DeepSpeed); familiarity with performance and deployment tools such as Triton is a plus. Comfortable working in a Linux environment.
• Proven ability to lead cross-functional teams on medium-to-large projects, taking accountability for outcomes and managing client interactions with strong communication skills.
• Experience deploying AI/ML solutions within highly regulated industries (e.g., financial services, healthcare, insurance) is a plus.
• Experience analyzing large, real-world datasets to understand their underlying context that applies to the model design and translate into effective model feature engineering. Prior experience with real-time payment fraud or risk transactional data, including data landscape knowledge and applicability to a problem domain across fraud and risk, is a plus.