November 19, 2019
Alan is a software engineer on the Shiny team at RStudio. In his 10+ years of experience in software development, he has helped build web applications, reporting pipelines, and many things between, including stints as a Staff Sergeant in the US Army and presidents of non-profit hacker and maker organizations. When his is not working, he likes to spend his time reading or being with his family. He holds a B.S. in Information Technology from Rochester Institutes of Technology.
1) What does data science mean to you?
I understand data science as the practice of trying to understand the world by looking for patterns in data. This is the central activity of science and has always been important to business, but in recent years I think there has been wider recognition of the fact that data science practices and tools useful in one discipline are likely useful in others. “Data science” seems like the term that best captures this growing cross-disciplinary recognition.
2) What do you think is the biggest challenge facing data science today?
Despite the fact that data science is an inherently highly technical activity, most of the tools from the computer science world for exploring data and managing complexity are not accessible to most data scientists. When they are accessible, it’s often through monolithic platforms that can’t be meaningfully modified or extended by end users.
I’m hopeful that as data science practice continues to mature technically, and as free software ideals permeate, specialized knowledge will continue to transfer to our wider community where it can grow and improve organically within this community of users.
3) How did you get started in the data world?
In 2015 I helped redesign the next-generation reporting data pipeline and API at Adzerk, an online ad company. Through that effort I interacted with the data scientists who would use the API and worked to develop a deep understanding of what features would be important to them. Along the way, I was exposed to various “big data” technologies myself, such as AWS Kinesis (for distributed event processing) and Redshift (a managed column store).
4) What is your work day like at work?
Like most at RStudio, I work from home, which rules. My day starts at 8AM, when I triage emails, GitHub, and Slack. At 9, I meet with others on the Shiny team. We summarize what we worked on in the previous day and what we plan to work on next.
After that I’ll work on a task, such as adding a new feature to a package like Shiny, reactR, or the forthcoming shinyloadtest. I might also review code or help a teammate.
When 4PM rolls around I put my computer in sleep mode and hang out with my family.
5) What is your favorite childhood toy? And why?
I really enjoyed Legos. I have a great memory of my mom helping me build a Lego pirate ship, and many more of building my own designs. Other than a computer, I think I might have been interested in Legos the longest of any toy during my childhood.