"Helping mission-driven organizations understand what their data is actually saying."
I don't start with dashboards, I start with the decisions those dashboards need to support.
I'm drawn to patterns, clean system design, and building data tools that help people make better decisions with clarity and confidence.
My relationship with math and data started early. On weekends, my dad would sit down with me and have me derive formulas and concepts from scratch — something I wasn't always thrilled about at the time. Looking back, I'm incredibly grateful for those foundational moments. They shaped how I think: not just learning what works, but understanding why and how it works at a fundamental level.
This curiosity led me to study Mathematics at the University of Waterloo, where I leaned deeper into first principles thinking and problem solving. During that time, I discovered data science as a natural intersection of my strengths: technical rigor, business intuition, and translating complexity into meaningful, actionable stories. What started as internships quickly became a clear direction.
In my most recent role, I gravitated toward data architecture and systems thinking: designing semantic models and building end-to-end data solutions across Sales, Marketing, and CX. I helped stand up the Marketing Analytics function from 0 to 1, creating foundational tools and dashboards that informed marketing strategy and campaign planning.
At my core, I turn patterns into insight and chaos into clarity, whether through deep-dive analysis, scalable data models, or tools that help teams confidently act on their data.
Outside of work, I'm naturally drawn to understanding how things work and how patterns emerge across even the most unexpected domains. I also love exploring well-crafted stories through books and movies. My favorite movie genre is "based on real life events."
Most data problems aren't actually data problems —
they're definition and assumption problems.
The biggest gaps rarely come from missing dashboards, but from misaligned understanding of what metrics actually mean and how data is captured in practice. I focus on getting to the root of those gaps by working from the lowest level of data and directly with the people closest to it. That's where the real friction and the real opportunity usually lies.
I don't start with dashboards, I start with the decisions they need to support. My work focuses on bridging the gap between how businesses think their data works and how it actually behaves in practice. By going to the lowest grain of the data and working closely with stakeholders and system users, I uncover misaligned definitions, hidden assumptions, and structural gaps. These are the kinds of issues that quietly cause data projects to fail adoption or lead to decisions based on skewed reporting.
My top 3 observations — and how I solve them
01 / Definition Gap
"I thought sales by order date meant when customers clicked 'order', not when it shipped from the warehouse."
"Our win rate is quotes won divided by quotes open… or is it quotes won vs. total quotes received?"
These misunderstandings are incredibly common and often go unnoticed until trust in the data starts to break down.
I always start by understanding the purpose of the metric. From there, I work backwards to define it clearly, align on assumptions, and document it with concrete examples that stakeholders can actually reference.
02 / Purpose Gap
"We need campaign metrics to see how our marketing campaigns did."
At first glance, this sounds straightforward. But without a clear goal, the project quickly becomes unfocused. Without a clear purpose, a data project turns into a merry-go-round.
I start by asking: what are you trying to do with this data? What decision are you trying to make? What initially sounds like engagement metrics might come down to something specific, like tracking promo code sales or campaign-driven revenue.
03 / Scope Creep
"Let's track Wait Time differently! Let's exclude IVR and focus on queue averages instead…"
When stakeholders with different responsibilities get involved, projects quickly become overloaded with competing definitions. You end up trying to make one dashboard answer fundamentally different questions.
You can't make one steak rare, well-done, and vegan at the same time. I protect clarity: one data view, one clear goal. Separate, well-defined views for separate stakeholder needs.
I work with teams to bring clarity to important business questions, operational reporting, and decision-making systems. My work usually falls into two areas:
When leadership teams need to deeply understand what is happening in the business and why.
Helping teams define and structure reporting systems they can actually trust and use.
I focus on ensuring that metrics reflect operational reality and support confident decision-making, not confusion.
I start with understanding the need. I work closely with stakeholders to uncover what decisions need to be made, how the business actually operates and what assumptions currently exist.
Case Study · Apr 2026
A data-driven case study exploring how digitization, ATS systems, and rising application volume have reshaped modern hiring dynamics in the U.S. labor market. Uses JOLTS data from the Bureau of Labor Statistics alongside LinkedIn membership as a proxy for the shift to digital recruiting.
Data Architecture · May 2026
A framework for separating call center metrics into three distinct analytical layers: customer experience (call-level), operational performance (queue-level), and agent effectiveness (agent-level). The goal is to ensure every metric answers a clear business question at the correct grain.
Data Architecture · In Progress
A recommendation system that acknowledges and counteracts recursive narrowing. Implementing latent preference modeling via Matrix Factorization. A human-centered recommendation design using the MovieLens 25M dataset. The question of what it actually means to recommend in a way that maintains exploration and novelty in a world of recursively narrowing systems.
✍️ First technical write-up coming soon
Coming soon
Taking full ownership of outcomes, starting from a clear purpose rather than a checklist of requests. I do my best work when I'm trusted to see something through end to end.
I genuinely enjoy building scalable, thoughtful solutions that support future needs. Quick ad-hoc fixes or short-term workarounds tend to drain my energy.
I do my best work when I understand the clear mission and purpose of a company. Knowing the broader direction of the organization drives the most effective prioritization for teams and individuals. The "why" of the org cannot be an afterthought.
Crafting compelling narratives that take others on a journey, helping them understand what the data is really saying and why it matters.
When direction changes rapidly and nobody explains why, it becomes impossible to prioritize thoughtfully. Context is everything. Without it, effort gets scattered and nothing gets done well.
Work stalls when nobody is sure who has the authority to decide. I thrive with clear accountability structures where ownership is explicit and respected.
Outputs need to serve a clear decision. When deliverables exist for their own sake — dashboards nobody consults, reports nobody reads — it's a signal that purpose was never defined upstream.
When teams can't openly discuss what went wrong, they can't fix what matters. The most important decisions often come out of honest conversations about failure. Cultures that suppress those conversations prevent real growth.
How digitization created a world where applying has never been easier, yet getting hired has never been harder — explored through BLS data, LinkedIn growth, and the rise of ATS.
Read the case study →The quiet assumption that breaks dashboards and trust simultaneously.
What I learned standing up a function from scratch — and what I'd do differently.
Start with Why
Simon Sinek
The Infinite Game
Simon Sinek
The Big 5 for Life
John Strelecky
The Cafe on the Edge of the World
John Strelecky
The Mom Test
Rob Fitzpatrick
Dare to Be Naive
Joshua Berry
Be Useful
Arnold Schwarzenegger
The Power of Now
Eckhart Tolle
The Art of Statistics
David Spiegelhalter
Becoming
Michelle Obama
The Light We Carry
Michelle Obama
Atomic Habits
James Clear
I'm open to data consulting, full-time roles, and interesting collaborations. It starts with a conversation.