Nathaniel D. Phillips, PhD
Data and Decision Scientist
Making science decision-ready.
I turn overwhelming data into clear tools people actually trust and act on.
The numbers behind the work
250M+
Patient records across EHR, claims, and survey data
500+
Students and professionals trained in data science and decision-making
10+
Organizations supported, from Big Pharma to early-stage startups
8+
Disease areas spanning oncology, neurology, and psychiatry
5+
Peer-reviewed publications in decision science, psychology, and health outcomes
5+
International conferences presented at, including INFORMS, Judgment and Decision Making, and rstudio::conf
My work follows a single thread: reduce overwhelming data complexity to its essential signal.
See the projectsOrganizations I've Worked With
How I work
Eliminate data distraction. Discover and promote key signals.
Most decision makers are drowning in spreadsheets and dashboards, yet still unsure what to do. Throwing everything into an algorithm and hoping for the best only deepens that confusion — and causes cost explosion. In most decisions, a handful of signals do all the work — the rest is noise. My job is to find those flags, cut through the noise, and build tools that make the right path obvious.
Build tools, not just analyses.
A one-off analysis answers one question. A well-built tool answers hundreds. I design reusable, open-source systems — APIs, dashboards, cohort builders — so teams can go from question to evidence without starting from scratch every time.
Help real, not idealized, people
A tool that maximizes a theoretical metric but gets ignored is a failed tool. I design with actual human behavior in mind — how people reason under uncertainty, what makes them trust a result, and where cognitive overload causes good analysis to go unused. The best output is the one that gets acted on.
Listen, learn, and teach
The most valuable expertise in any organization often belongs to the quietest people in the room. I listen carefully — to experts, skeptics, and end users alike — and test my assumptions before drawing conclusions. I meet people where they are, without jargon or pretension, and invest in teaching others so that insight doesn't stop with me.
Let's cut through your data — together.
Whether you're a pharma team designing a real-world evidence study, a startup building analytics infrastructure, or a researcher looking for a collaborator — I'd like to hear what you're working on.
Fun fact: I wrote a free, open-source statistics textbook called YaRrr! The Pirate's Guide to R. It's been used by thousands of students worldwide. Yes, there are pirate jokes.
Read it