What a Data Linguistic Analyst at Meta Wishes They Had Known Before Entering the Data Industry

Scott, a Data Linguistic Analyst at Meta, advises aspiring professionals to prioritize research experience and publications over solely focusing on academic coursework, emphasizing that "companies value the independent research" demonstrated in a thesis or published work. The pursuit of impactful research projects, even if personally challenging, as highlighted by Scott's own experience hitting a "roadblock" due to compute resources, is crucial for career advancement in the field.

Data Analysis, Research, Publication, Resource Management, Project Management

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Scott N.

Data Linguistic Analyst

Video Highlights

1. The value of research experience and publications is highly emphasized in this field, even more so than the degree itself. Consider pursuing research internships and focusing on projects with publication potential.

2. Computational resources can be a major roadblock in data analysis projects. Early experience working with large datasets and finding solutions to computational limitations is important.

3. It can be challenging to maintain motivation for complex projects; finding a pet project and collaborating with others can help overcome this and allow you to create a portfolio that showcases your skills and abilities.

Transcript

What have you learned about this role that you wish someone would have told you before you entered the industry?

I'd like to go back to what I was saying about higher education. A lot of companies value master's and PhD degrees because they recognize the independent research involved in obtaining them. To complete these degrees, you need a thesis, and sometimes a master's as well.

Some companies will even state that they expect a master's or PhD, or that you've published. Had I known this in my junior and senior years, I would have pursued research internships more vigorously than my schoolwork. I was genuinely interested in research and had the opportunity to perform intensive statistical analysis on time series data.

There was a moment when I had the opportunity and asked if it was possible to do this. They said it was fine and not to worry about it. I thought I didn't have to spend my time on that. The next research opportunity I had involved working on a project that likely would have been published if it had been successful.

However, I encountered a roadblock that stopped me in my tracks. I didn't necessarily reach out for help as much as I should have. For students interested in these jobs and looking at master's degrees, consider how difficult it is to get into prestigious, elite, and expensive schools.

Try to get published. Looking at entrepreneurial ideas works similarly to thinking about research ideas. You need to research thoroughly to understand the landscape and identify niches where you might find something. Access to data is also a significant factor.

The roadblock I mentioned was due to compute resources. I had everything prepared, but when I ran my program, it would take 36 days to complete. I didn't have what I needed, but it's something I could have overcome. I'm still working on it.

Find those pet projects. It doesn't have to be something you dedicate your entire life to; it could be something you do on weekends if you have nothing else. It can be challenging to find the motivation for something mentally taxing.

However, when you consider the significant payoff it could offer down the line, you can take pride in it and have your name on it. You can also involve other people; you can accomplish much more with just a few collaborators. That's what I recommend.

Advizer Personal Links

scottn66.GitHub.io