Dan McCarthy

About Dan McCarthy

Vision scientist and former neuroscience professor 🧠 turned XR nerd with 15+ years of experience in mixed-methods research and data science, working at the intersection of ML/AI, human behavior, and decision-making.I love data. I also love people and understanding the \'why\' behind behavior.My perception of purple isn’t yours. Neither is my 4 on a Likert scale, or my pattern of neural activity when listening to a favorite piece of music.Why is it a 4? Why do you love a song or a movie that others loathe? Those differences matter.I’m driven by consilience of knowledge — the idea that deeper understanding comes from integrating physical, social, cultural, and still-unknown variables. The whole story always tells us more than a single chapter.I’ve led high-impact, internationally recognized research on how perception and attention guide decision-making, securing $250k+ in grants and multiple awards for outstanding contributions. Today, I apply that same rigor to real-world products — helping teams move fast, reduce risk, and unblock decisions under tight deadlines.ML/AI, statistics, and Bayesian inference are my jam. Python, MATLAB, and SQL are my tools. Human context is essential to triangulate the quant with the qual.I’m not all users. My intuition isn’t yours. Nothing is one-size-fits-all.A data-driven, human-centered approach is the closest signal we have to building technology that’s inclusive, trustworthy, and a little more fun.

Experience

1. Senior Ux Researcher (Contract Via Teksystems)

   Meta

   May 2025 — Present · Seattle, WA, US

   Own applied research to quantify product health for wearable device hardware (Meta Ray-Ban Display, Meta Neural Band) and software (Meta AI, OS, APIs) from inception to live market. Design and execute scalable evaluation metrics using surveys (Qualtrics) and usability tests to quantify user satisfaction, friction points, and establish KPIs.Use Python and to build adaptable statistical (e.g, parametric, non-paremetric, Bayesian) and machine learning pipelines (e.g, drivers, factor analysis, latent constructs) to provide quant data within a business day of study completion. Triangulate qualitative themes with quantitative data to identify risk, determine reliability gaps, and focus research initiatives on key product features.Analyze SQL-based datasets and maintain dashboards to monitor ecosystem-level usage, retention, and system-level failures.

Education

• University of Nevada, Reno

  Ph.D., Cognitive & Brain Sciences

  2010 — 2014

• Brown University

  Postdoctorate, Cognitive Neuroscience

  2014 — 2018

• University of Northern Iowa

  M.A., Psychology

  2008 — 2010

• Grand View University

  B.A., Major: Psychology, Minor: Spanish

  2003 — 2007