I'm a data scientist, astronomer, and chronic overthinker who's spent the last decade trying to figure out why people do what they do—not just what they do.
Most of my professional work has been in games and educational technology, building experiments, measurement systems, and models that help teams understand their users and make better decisions. But before I got into industry, I spent years studying stars, teaching physics, running a public observatory, and applying machine learning to clinical neuroimaging data. All of which is to say: I've been looking at complex systems and trying to make sense of noisy data for a very long time.
What I'm Actually Trying to Do
Here's the thing: I think my job is to help people understand things. Not to build models that make decisions for them. Not to automate away judgment. Not to produce dashboards that tell executives what they want to hear while obscuring what's actually happening.
I want to know why one feature gets ignored while another sees constant use. Why some users convert and others don't. What's broken, why it's broken, and whether we can actually fix it—or if we're just going to pretend the metric we're optimizing for is the thing that matters.
My role is to create knowledge that makes other people's work clearer and their decisions better informed. To translate what users are doing into insights about what they're trying to accomplish and where our products are failing them. To help designers, engineers, and product teams understand the gap between their intentions and what's actually happening in the wild.
But I'm increasingly convinced this puts me at odds with a lot of what "data science" has become. There's a whole contingent of the field that's really just building increasingly sophisticated ways to avoid thinking—to hand off responsibility to algorithms while maintaining plausible deniability. To produce analysis that looks rigorous but is actually just pandering bullshit designed to flatter management and confirm decisions they've already made.
I'm not interested in that. At all.
How I Think About Analysis
I approach data analysis as a process of knowledge creation, not pattern extraction—and I think the distinction matters more now than ever. We live in a world drowning in data but starving for understanding, where algorithms claim to "let the data speak for itself" while actually encoding the assumptions and incentives of whoever built them. Data doesn't explain behavior on its own. It reflects the interaction between people, systems, and the ideas or incentives that shape their choices. If you don't understand the system, you're just finding patterns in noise.
This means meaningful analysis starts with theory—with domain knowledge, an understanding of human motivation, and a clear mental model of what's actually happening. Before I ask "what are users doing?" I ask "who are these people, and what constraints are they operating under?" Segmentation isn't just a clustering exercise; it's a hypothesis about how the world works. Groups aren't accidents in the data—they're populations bound by shared goals, constraints, or contexts. Whether I'm analyzing players, learners, research subjects, or customers, I treat defining these groups as an act of scientific inquiry, not statistical convenience.
From there, I focus on relationships: between who people are, what they do, and what happens as a result. Funnels, retention curves, and predictive models are tools for surfacing these relationships, but they're worthless without context. I want to know what motivates people, what trade-offs they face, and how the structure of the product or environment shapes their decisions. This perspective draws as much from psychology and domain expertise as from statistics—because understanding behavior requires understanding people, not just their clicks.
I'm particularly skeptical of claims about causality that aren't grounded in experimentation or counterfactual reasoning. Observation alone is insufficient. You need experiments, careful metric design, and an honest accounting of what you can and can't conclude from your data. Even well-designed experiments only tell you part of the story—causality emerges most clearly when you reconcile quantitative results with qualitative knowledge of the system, its mechanics, incentives, and failure modes.
This philosophy has been consistent across everything I've done: astrophysical simulation, medical machine learning, live service games, educational products. The contexts change, but the goal stays the same: build models and analyses that are not only statistically sound, but conceptually honest. Grounded in how real people engage with real systems. Capable of supporting decisions that actually matter.
I care about this because data science done poorly is just sophisticated bullshit. And too often, it's pandering bullshit—designed to flatter management, feed egos, and create the illusion of rigor while avoiding any actual insight that might be uncomfortable or inconvenient. Done well, though, it's a tool for building better understanding and making better decisions. That's what I'm trying to do.
What I Care About
What drives me is the same thing that got me into science in the first place: I want to understand how things work, and I want to help other people understand them too.
I'm drawn to problems at the intersection of human behavior, system design, and measurement—places where understanding the structure of the system is as important as analyzing its data. I like work that requires both curiosity and rigor, where you need to know when to trust your intuition and when to demand proof, and where the goal is to enable better decisions rather than automate them away.
I also care deeply about making space for different ways of engaging with systems. Whether it's pushing back on the idea that there's only one "right" way to play a game, or questioning whether optimization is actually the point, or just refusing to accept that complex tools can only be used in rigid ways—I think a lot of received wisdom in technical communities is less about what's true and more about what's comfortable for the people saying it. And I'm not particularly interested in being comfortable if it means pretending everyone has to care about the same things I do.
As I look for my next role, I'm looking for teams that value understanding over automation. That want analysts who ask hard questions, not ones who produce pretty charts that confirm existing biases. That see human judgment as essential, not something to be optimized away.
Outside Work (Or: The Things That Actually Define Me)
In a lot of ways, I think the things I care about outside of work say more about who I am than anything in my professional life.
Astronomy has been part of me for as long as I can remember. I grew up watching the Moon from my bedroom window, seeing comets dominate the night sky, gazing at the winter Milky Way from my snowy backyard in rural Atlantic Canada. I studied it in university, did my master's degree in it, and spent years running one of Canada's largest planetariums—showing thousands of people the stars overhead, the planets, the galaxies, everything we can observe in the universe above. I got to do real scientific research, operate observatory telescopes, give public tours. My dream was always to be a professional astronomer. That didn't work out—I didn't get selected for a PhD at my school, and I chose to stay home and make a life here rather than chase that dream somewhere else. It's one of my deepest sadnesses, but also one of the things that made me who I am. I still stare at the sky whenever I get the chance. Once an astronomer, always an astronomer.
Baseball is, in a very real and visceral way, who I am. I've been playing in some capacity since I could walk—whether that's by myself in the backyard, in little league as a pitcher, in video games, or now in recreational softball 2-3 times a week every summer. I still remember clearly (and occasionally rewatch) the Blue Jays' back-to-back World Series victories in 1992 and 1993. I follow the Jays as much as I can. There's something about the rhythm of the game, the elegance of the mechanics, the way it unfolds that just speaks to me in ways nothing else quite does.
Star Trek -- specifically 90s Trek: The Next Generation, Deep Space Nine, and yes, even Voyager -- formed the very core of my personal politics. These shows, TNG especially, taught me that humanity deserves to be free, and that we can only be free if we're not strapped to the yoke of unchecked materialism and unjust, or non-consensual, power hierarchies. It's idealistic, sure, but it's the kind of idealism that asks: What if we actually believed people were worth investing in? What if we built systems that served human flourishing instead of extraction? Those questions shaped how I think about everything, including what kind of work I'm willing to do and what kind of world I want to help build.
Video games have been my constant companions since I was maybe four years old, playing the original Legend of Zelda on the NES. I've been a lifelong Zelda fan—A Link to the Past and Ocarina of Time are games I've replayed so many times I've lost count. The original Super Mario Bros., Mario 2, Mario 3, Super Mario World—I've played them so much I probably dream about them. The Super Mario Bros. 2 overworld theme still gets stuck in my head 35 years later. And Mario Kart? Easily my favorite franchise of all time. I bought a Wii U expressly to play Mario Kart 8. I've probably logged more hours into Super Mario Kart than any other game ever. Beyond Nintendo, I have deep, formative memories of Final Fantasy VII (which I'm replaying for the first time in 25 years and loving just as much as I did in junior high), point-and-click adventure games (probably my most beloved genre conceptually, even if I haven't played that many), and the mid-90s FPS trinity of Doom, Doom II, and Duke Nukem 3D—games I used to play in the computer lab with friends, in dial-up deathmatches, and that I still return to 30 years later.
I also GM TTRPG sessions for my friends and family, Pathfinder 2e specifically. My home game is set in a homebrew world (Ardrigail) based on Hyrule from Ocarina of Time. I love the system, but I find myself sharply at odds with a lot of the online community that treats it as a combat engine first and a roleplaying game last. To me, it's a rich and reliable toolkit for collaborative storytelling that happens to have robust tactical rules when you need them, not the other way around. The prescriptivism and gatekeeping I see in a lot of TTRPG spaces frustrates me endlessly, which is probably why I keep writing about it.
All of this -- the Star Trek, the games, the baseball, the stars -- feeds into how I think about my work. I'm constantly asking: What are the systems at play here? What are people trying to accomplish? Where does intention meet reality, and what happens in that gap? Whether I'm analyzing user behavior in a game or thinking about why someone swung at a pitch outside the zone or pondering how light from a distant galaxy reached my eye, it's all the same curiosity. The same need to understand.