Practicum Pride: Mozilla
Sarah Melancon came to the MSDS program with a BS in Mathematics from the University of Utah and Brian Wright came to the MSDS program with a BS in Mathematics & Computer Science from the University of Redlands. Continue reading to learn more about their practicum experience at Mozilla!
Can you tell us a bit about Mozilla? What is it like working there?
SARAH — We love it there! It’s a pretty big company, so I’m constantly meeting new people who are working on cool projects.
Can you describe the project that you are working on?
SARAH — We’re building a tool to help Firefox add-on developers understand how people are using their product. Currently, add-on related data lives in several different databases, and Google Analytics. We are writing code to combine all of this data into one dataset and productionizing our code to update the dataset daily. The final product will be a single dashboard that an add-on developer can visit to make informed product decisions.
How are you applying the knowledge gained from the program to your practicum?
BRIAN — I would say Distributed Computing has been the most helpful. We use a lot of Spark at Mozilla and deal with lots of data, so it is important that our code is efficient.
SARAH — I would agree with Brian, Distributed Computing has been the most helpful course. Relational databases has also been helpful because we use a lot of SQL.
What is it like working with professional data scientists?
SARAH — Everyone is really collaborative at Mozilla. Even though we are just the interns, people treat us like part of the team and data science moves really fast, so everyone is always learning.
BRIAN — It is great, the data scientists at Mozilla are helpful whenever Sarah and I need help, but they also let us work autonomously, making for a good balance of offering support, but not being too overbearing.
What is the biggest challenge you’ve faced at your practicum?
BRIAN — We are productionizing our code, so it’s important that it not only runs, but it also must follow code standards, and be both easily interpretable and efficient.
SARAH — Even though we have a great mentor, I think you have to be more self-sufficient in the workplace than in school. At school, if you get stuck you can usually get help from the professor. In the workplace, sometimes you are the first data scientist to work with a particular data source, so there is not always an expert to look to for help, and you have to try to become the expert. It’s really rewarding, but sometimes challenging!
Are there any cool perks?
BRIAN — Free lunch and good desserts! They also have an espresso machine, so we can stay caffeinated.
