About

How I Think About AI

Over time, my focus shifted from building models to understanding how organizations successfully adopt AI.

My work has focused on scientific AI, clinical development, healthcare operations, and the evaluation and governance systems required to deploy AI responsibly.

My path into AI started in the sciences. I began as a pre-med student at Virginia Tech and took graduate bioinformatics courses at Johns Hopkins. Along the way, I discovered that I loved thinking about algorithms, systems, and learning to code, then applying that code to analytics and decision-making problems. That realization led me to NC State's Institute for Advanced Analytics and a career at the intersection of science, technology, and healthcare.

That scientific background still shapes how I approach AI today: grounded in evidence, constrained by real-world systems, and focused on decisions that have to hold up outside the notebook.

The journey was not linear. It took two attempts to get into NC State. I talked my way into my first analytics interview with Deloitte. It took two separate interview processes spread across nearly two years before I joined McKinsey. Many of the opportunities that shaped my career arrived on the second attempt.

Those experiences left me interested in systems as much as models. How do organizations make decisions? How do teams adopt new tools? How do we know whether AI is creating value rather than simply producing more output?

What I find most compelling about AI is that it can improve continuously through better data, evaluation, and feedback. Few technologies make it possible to measure performance, learn from real-world use, and systematically improve over time. I enjoy building those feedback loops as much as building the models themselves.

Today, I focus on evaluation, governance, and organizational adoption. Organizations rarely struggle because models are weak; they struggle because accountability, reliability, and putting AI into production are hard.

Designed & Measured AI is where I think through those questions in public. The essays focus on evaluation, governance, operating models, and the practical realities of deploying AI in environments where reliability matters more than novelty.

Selected Experience

Over the past decade, I've helped organizations move from analytics experiments to production AI systems that improved real-world decision making, leading teams, products, and transformation programs that generated hundreds of millions of dollars in measurable value.

Across consulting, product leadership, and enterprise AI, the work has remained consistent: translating advances in AI into production systems used in healthcare and life sciences.

• Head of AI Enablement & Data Science Ellipsis Health · Series A HealthTech · 2026

  Built the evaluation, governance, and deployment infrastructure for clinical voice AI at a CB Insights AI 100 company, establishing the standards used to assess model quality, safety, and production readiness before patient-facing release.

• Principal Product Lead, AI & Applied Science McKinsey · Scientific AI 2024–2026

  Led AI product teams across indication discovery, formulation AI, clinical trial acceleration, and biomedical literature intelligence. Built reusable Scientific AI platform capabilities and led multiple products from concept to production, accelerating R&D workflows and therapeutic decision-making.

• Expert (Engagement Manager) McKinsey · LifeSciences.AI 2022–2024

  Led cross-functional teams of 15–20 data scientists, engineers, and domain experts delivering enterprise AI transformations across healthcare and life sciences. Directed programs generating hundreds of millions of dollars in value, including the first global operational rollout of an AI product within a major pharmaceutical organization.

• Senior Data Scientist McKinsey · QuantumBlack 2020–2022

  Led AI workstreams across healthcare, life sciences, and financial services. Delivered large-scale patient and customer analytics programs operating at 5–10 million member scale and contributed to a financial services transformation generating more than $100 million in incremental revenue.

• Senior Data Scientist Deloitte Consulting 2018–2020

  Developed and deployed machine learning solutions for healthcare organizations, including readmission risk prediction and population health analytics. Served as National Learning Director for Deloitte's AI training program, teaching production machine learning to more than 100 data scientists nationwide.

Principles

• Start with the decision, not the model.
• Measure AI by improvements in decisions and outcomes, not output.
• Production is where AI creates value.
• Technology succeeds when organizations adopt it.
• Trust is built through evaluation, transparency, and reliability.

Education

• MS Analytics NC State, Institute for Advanced Analytics 2018
• BS Biochemistry Virginia Tech 2014

Credentials

• INFORMS Certified Analytics Professional — Expert
• AWS Certified Machine Learning Specialty
• AI/ML in Medicine, Cambridge Centre for AI in Medicine
• Prior Labs Community Ambassador
• Bioinformatics graduate coursework, Johns Hopkins University