About Me

I’m a Lead Data Scientist with 10 years of experience delivering data science and software solutions in insurance and broader financial services.

In my current role, I’m a technical lead on a cross-functional AI team, shipping generative AI systems that improve cycle time and unit cost in regulated workflows. I’ve led document intelligence initiatives from prototype through production, driving measurable operational gains—including solutions that have dramatically reduced processing time and eliminated significant vendor costs by bringing capabilities in-house.

My career spans the full analytics spectrum. I started in business intelligence and reporting, building dashboards and automating workflows. I then moved into more traditional data science—building mortality projection models with generalized additive models, conducting statistical analyses for underwriting decisions, and designing data pipelines. I earned my M.S. in Statistics from Texas A&M in 2021, which deepened my foundation in statistical modeling and inference. Over the past few years, my focus has shifted to operationalizing modern AI, particularly generative AI and document processing capabilities on AWS.

My current focus is on operationalizing AI responsibly: defining clear success metrics, building task-specific evaluation sets, instrumenting monitoring and human-in-the-loop review where needed, and establishing maintainable engineering patterns (testing, deployment automation, and reliability practices) so solutions are scalable and auditable.

I have substantial cloud experience on AWS across managed AI services and serverless/workflow orchestration, strong Python and data engineering fundamentals alongside applied ML, and both AWS Cloud Practitioner and Machine Learning Specialty certifications.

I’m looking for tech lead or data science leadership roles where I can scale this work—building teams, platforms, and governance that make AI delivery fast, reliable, and durable.