Building data products that inform and scale
Senior Data Scientist with 12 years across healthcare AI, fintech, and product analytics. I build data products from concept to production — RAG assistants, risk models, evaluation frameworks, and clinical analytics tools.
About me
I'm a Senior Data Scientist based in Dallas, TX, with 12 years of experience building data products at PathAI, Tempus AI, Mercury, and Meta.
My work spans healthcare AI, fintech risk, and product analytics. I've built RAG-based assistants for clinical and scientific users, designed LLM evaluation frameworks for chatbot quality, and developed risk models and experimentation systems that informed product decisions at scale.
When I'm not building data products, I'm thinking about model reliability, evaluation quality, and how to make complex AI systems trustworthy and understandable to the teams that use them. I hold an M.S. in Data Science and a B.S. in Computer Science from The University of Sydney.
2024–Present
Senior Data Scientist at PathAI — AI pathology analytics, RAG tools, LLM evaluation frameworks.
2021–2024
Senior Data Scientist at Tempus AI — real-world evidence, oncology analytics, clinical AI tools.
2018–2021
Data Scientist at Mercury — fraud risk models, growth analytics, internal chatbot prototypes.
2014–2018
Data Scientist at Meta — large-scale product analytics, A/B testing, NLP classification.
2012–2014
M.S. Data Science — The University of Sydney.
2008–2012
B.S. Computer Science — The University of Sydney.
How I work
Statistical Rigor
Evidence-based by default. I design experiments with proper controls, apply causal inference, and validate model assumptions before making recommendations.
Healthcare AI Impact
Built RAG assistants and analytics tools that give clinicians and scientists fast, trustworthy access to model outputs, cohort definitions, and validation results.
LLM Evaluation
Designed evaluation frameworks assessing retrieval accuracy, answer faithfulness, hallucination risk, and citation quality — keeping AI products honest and reliable.
Experimentation Culture
Designed A/B tests and lifecycle experiments across onboarding, risk, and product features with rigorous metric selection, power analysis, and clear readouts.
Data Products
From concept to production: risk models, dashboards, cohort tools, and evaluation pipelines used daily by product, risk, clinical, and executive teams.
Cross-Team Partnership
Strong collaborator with product managers, engineers, clinicians, and executives — translating complex data findings into clear, actionable decisions.