Zachary Beaver graduated from the School of Information with a Master of Information and Data Science in 2015. He currently works at Google as a Senior Data Scientist, working on building and deploying computer vision models on mobile devices. Prior to the I School, Zach graduated with a Bachelor of Science in Biology and a Minor in Computer Science from Wofford College.
Why did you choose the I School?
I chose the I School because of its reputation in the Valley. Being excited about Data Science and wanting to work for companies at the field’s bleeding edge, I was looking for a program that had its finger on the pulse of the skills employers were seeking, along with a history of producing graduates who excelled in industry. Berkeley (and the I School, in particular) were a perfect match.
What made the I School special?
The friendships I developed with other students and staff are by far the best thing about the I School. A few years after graduation, I still meet regularly with friends from my cohort to share knowledge and opportunities, as well as simply hang out and enjoy each other’s company. Also, the staff at career services are extremely caring and knowledgeable. They spent hours reviewing my resume, networking on my behalf, and ensuring that I was successful in my job search after graduation.
What was your favorite class at the I School?
Field Experiments was an incredible class. On a practical level, it’s about teaching how to design, implement, and analyze the results from experiments. But more generally, it teaches how to think critically about causality and assess truth through experimentation. I find myself applying the practical skills and mental model constantly in my work.
What are you working on now?
I’m currently developing computer vision algorithms to help people better understand their health. My team sits at the intersection of applying recent breakthrough advancements in image and video understanding to infer valuable health knowledge. I can’t be more specific than that (yet!), but I love working on knotty technical problems that have material benefits. It's fun and challenging to figure out how “academic breakthroughs” can be applied and modified to build products that provide real benefit to others.
Do you have any advice for aspiring data scientists?
Build constantly and collaboratively! A body of work that solves real problems and demonstrates technical competence is invaluable. However, doing this by yourself doesn’t allow for rapid feedback and ideas from others that can improve your work and skills. If you can build a body of data science work in collaboration with others, you learn faster, are held accountable to deadlines, and start creating a network that can help you find opportunities and serve as references.
