Dr. Daniel Wooten
Principal Data Scientist | Card AI Research & Development
Daniel Wooten
About Me
I transform complex research into high-impact financial solutions.
With over 7 years of experience developing, deploying, and analyzing AI/ML models, I specialize in the bridge between R&D and production. My recent work at Capital One focuses on leveraging Generative AI and Transformer architectures to solve multi-million dollar challenges in risk and operations.
From whiteboard concepts to government-audited production models, I have led projects through every stage of the development pipeline. My focus is on data-adaptive algorithms that don't just find patterns, but directly inform critical business decisions and drive massive efficiency gains.
Current Impact
Principal Data Scientist at Capital One
(Card AI R&D)
Project OWL: LLM-as-a-Judge
I developed a sophisticated LLM "judge" to evaluate complex call transcript analyses. Through iterative prompt engineering and meta-prompting, I achieved human-level performance, netting the company $30 million in annual savings.
I developed a sophisticated LLM "judge" to evaluate complex call transcript analyses. Through iterative prompt engineering and meta-prompting, I achieved human-level performance, netting the company $30 million in annual savings.
Project Genesis: Acquisitions Risk
Leading the use of Transformer architectures on raw credit bureau data to predict charge-off risk. By capturing sequential payment patterns, our POC outperformed SOTA GBM models, representing a $15 million annual savings potential.
Leading the use of Transformer architectures on raw credit bureau data to predict charge-off risk. By capturing sequential payment patterns, our POC outperformed SOTA GBM models, representing a $15 million annual savings potential.
From the heart of AI
Ph.D. from UC Berkeley
My foundation in large-scale data began during my Ph.D. at UC Berkeley, where my thesis centered on 1000 GB+ datasets. As an original member of the Berkeley Institute for Data Science, I integrated machine learning and computational engineering to solve problems at the bleeding edge of the data revolution.
Foundational Experience
AI/ML Engineer at Perceptronics Solutions
Architected Bayesian inference systems and real-time city-wide tracking solutions. My work involved translating subject-matter-expert intuitions into actionable algorithms, utilizing Multiple-Hypothesis Tracking and Generalized Multi-Bernoulli filters for unlearnable targeting environments.
What do I bring to your team?
A rare combination of research depth and commercial execution.
Quantifiable Value
I focus on models that move the needle, with a track record of delivering 8-figure cost savings.
Strategic Leadership
Experienced in advocating for SOTA methods (Transformers/LLMs) to executive leadership.
Rigorous Research
UC Berkeley trained with the ability to implement the latest architectures from paper to production.