Who am I?
I’m an ML engineer with a strong foundation in statistics and a practical approach to solving hard problems. I’m drawn to systems that need both structure and flexibility, where success means translating messy data and ambiguous goals into clear, measurable outcomes. I work best as an individual contributor but collaborate closely across functions when it helps move things forward. I care about depth, clarity, and impact, not bells and whistles.
What have I done?
I’ve built ML systems at scale, powering ranking and search for high-traffic consumer products. My focus has been on improving relevance, modeling user behavior, and driving measurable business impact. Before transitioning into ML engineering, I spent several years in data science, designing experiments, analyzing noisy behavioral data, and influencing product decisions through sharp, principled analysis. I’ve also spent enough time wrangling real-world data to know that clean theory rarely survives contact with production. I know what it means to take something from scratch, whether it’s a model, a research paper, or a production system, and drive it through to something that delivers value. My PhD in statistics trained me to think deeply about uncertainty and signal, and I bring that same lens to the systems I build today.
What do I care about?
I care about precision in thinking, elegance in implementation, and results that actually matter. I’m drawn to technical problems that resist shortcuts, ranking trade-offs, ambiguous metrics, hard-to-measure objectives. I also care about team culture: honest reviews, low-ego collaboration, and creating space for people to grow through ownership, not hierarchy.
What do I stay away from?
Overcomplicated solutions. Performative work. Meetings that go in circles. I have little patience for shallow metrics or cargo cult engineering. I prefer clear direction, sharp feedback, and working with people who know when to sweat the details, and when to move on.
Why do I mentor?
Because helping someone navigate a career transition or make sense of the tech landscape is deeply rewarding. I enjoy breaking down what often feels opaque—whether it’s resumes, interviews, or personal positioning, into clear, actionable steps. Watching people gain clarity and confidence in their path is what makes it worth it. I also see mentoring as a way to give back to the tech community that has shaped my own journey.
What do I do outside of work?
I like things that clear my head while keeping me just engaged enough. Yoga helps me slow down and stay balanced. Juggling started as a random side hobby but turned into a nice way to train focus and coordination. Hiking gives me space to think, some of my best ideas show up somewhere between trail markers.
