My Story, From curious kid to data strategist
Every problem is a puzzle waiting to be solved
I believe the best technology solutions come from truly understanding people—what they need, how they work, and what keeps them up at night. This is how I got here.
The Journey
How a fascination with puzzles became a career in solving data challenges
The Early Years
I was the kid who took apart every electronic device I could get my hands on. My parents weren't thrilled, but I was learning something crucial: complex things are made of simple parts that work together.
"First lesson: Everything can be understood if you ask the right questions."
University & PhD
My PhD taught me that data science isn't just about algorithms—it's about asking questions that matter. I spent years learning not just how to code, but how to translate business needs into technical solutions.
"Key insight: The best technical solution is worthless if people can't use it."
Real World Impact
Working with companies from startups to Fortune 500s taught me that every organization has the same core challenge: turning their data into decisions. The tools change, but the human element remains constant.
"What matters: Success is measured by impact, not technical complexity."
How I Approach Problems
My process has evolved through years of both failures and successes
Listen First
Before touching any code or data, I spend time understanding the real problem. Often what people think they need isn't what will actually solve their challenge.
"A retail client wanted "better forecasting algorithms." After digging deeper, I discovered their real issue was inventory managers who didn't trust the current system. We focused on transparency and user training—sales improved 23% without changing a single algorithm."
Start Simple, Then Scale
I've learned that the most elegant solutions often start with the simplest possible approach. Once we prove the concept works, we can add complexity where it actually adds value.
"Instead of building a complex recommendation engine from scratch, I created a simple prototype in five days using existing tools. User testing revealed that explainability mattered more than accuracy—so we shipped the simple version that people actually trusted."
Build for Humans
Technology is only as good as the humans who use it. Every solution needs to fit into people's workflows, not the other way around.
"An ML pipeline I built now serves 2 million predictions daily, but its success isn't in the technology—it's in the automated alerts and simple dashboards that help the team trust and act on what the system tells them."
My Toolkit
I've learned these tools through real projects—successes and failures both
Data Strategy
Data Foundation
ML Engineering
Frontend Design
MLOps & DevOps
Data Product Management
Experimentation
Agile Delivery
I choose tools based on what actually works for the people using them, not what's trending on Twitter.
What I've Learned
People first, technology second
The best technical solution is the one people actually use.
Start with why
Understanding the real problem is more important than any algorithm.
Fail fast, learn faster
Every mistake taught me something I couldn't learn from success.
I'd love to hear from you
Whether you're dealing with a specific data challenge, thinking about your strategy, or just want to chat about the intersection of technology and human needs—let's connect.