Alumni Spotlight: February 2019 – Dr. Mark White

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Analytics Engineer at a progressive, digital communications agency.

Alumni of the Department of Psychology

What was/is your area of study?

My Ph.D. is in social psychology. I primarily researched the pernicious ways that people try to justify expressing their prejudices, but I also published studies on topics like intergroup emotions, dehumanization, and political psychology. In seminars, I gained expertise in broader theoretical areas like the psychology of attitudes, norms, and behavior. The majority of my graduate coursework, however, ended up being in statistics or research methods—this area of study has been the most applicable to what I do in my career. Before graduate school, I earned bachelor’s degrees in both psychology and sociology.

 

What is your current career or position title?

 

I use data to answer questions and solve problems that deal with attitudes and behaviors. I tell people that I’m in “data science” if I only have a few seconds to say what I do, but this term is so imprecise that I try not to use it much. I describe myself as a “quantitative social scientist” as a way to touch on my expertise in both quantitative methodology and the social sciences.

 

I’m currently the Analytics Engineer at a progressive, digital communications agency that works with candidates, causes, and corporations. During the 2018 election cycle, our company did work in 139 races—including House, Senate, and governor races. I am fortunate to be on a team that did our small part to help accomplish things like flip the House back to a Democratic majority and ensure important ballot measures passed (such as one prohibiting discrimination on the basis of gender identity).

 

What do you do?

 

I help with the statistical methodology and programming in testing and measurement; the primary two questions my team tries to answer are: Is the campaign working? How do we run campaigns more efficiently and effectively?

 

One of the main things our team does is run online experiments to assess the causal effect of our advertisements on the audience. We randomly assign people to either see the ads in our campaign (i.e., a treatment group) or to not see them (i.e., a control group). People will either see our ads (or not) over the course of days or weeks, and then we survey them afterward. We usually ask questions measuring if people have heard of or feel favorably toward the candidate, cause, or brand for which we were advertising. If you have ever seen a question appear before a video, these are the types of questions we look at.

 

I learned the R programming language in my graduate courses, and I use this in analysis and product building. For example, I have used this to build web apps for my team to use in processing and analyzing data from experiments. I used all of the knowledge I learned from graduate school in designing and analyzing experiments to inform how this app works. Most of my time is spent running statistical analyses, programming data-oriented applications or dashboards, text mining, building machine learning models, or designing surveys.

 

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What is your favorite aspect of your job/career?

 

In no particular order:

 

  • The city I work in and the people I work with. I took this job without having ever visited New York City, and I fell in love with the city almost immediately upon visiting for the first time. When I thought I was going to be a professor, I knew I wasn’t going to be able to pick where I lived—so I really do not take this for granted. My team and coworkers are also filled with intelligent and friendly people.

 

  • Being the expert in a specific area. I was the first person to be hired with a Ph.D. at this company. This had positives and negatives to it, but I like being a person that people feel as if they can come to if they have a data or research or programming question. This keeps things fresh, as well, because I am constantly getting new problems to work with.

 

  • Learning new things. Being this person that people come to with questions means that, inevitably, I will be presented with new problems that I do not know how to approach at first. I loved my statistics courses in graduate school, and I very much enjoy keeping up my continuing education by learning new techniques, models, approaches, languages—whatever is needed—to solve interesting problems.

 

  • Statistics and research. Crunching numbers, writing code, designing studies, and writing-up results were my favorite parts of academic life. Now, these tasks take up the vast majority of what I do every day.

 

  • Building useful tools and doing applied work. A lot of academic work I did was theoretical: We came up with interesting ideas for how the world works, we ran studies, we wrote these down, and then—if we were lucky—we got them typeset into PDF files and put behind a paywall. It is very rewarding, then, to now program a tool that my coworkers use and come to me and say, “This has made my day at work go smoother.” It also is freeing to be held more to a standard of being useful and actionable, whereas the constraints of doing basic research made me feel pressured to do work based on more novel and counterintuitive ideas. Especially in the crazy political times we live in, I like playing my (very small) part.

 

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What advice do you have for current graduate students?

 

Again, in no particular order:

 

  • Start a blog. It is an online portfolio of your skills, while also being fun. Do the things you like doing on it, while showcasing your ability. I love statistics, but also basketball; so, I write a lot of posts analyzing NBA data. I also like learning new statistical and machine learning techniques, so I teach myself them and write explainers. Blog about whatever it is that you like doing that also showcases your skills to future employers. During job interviews, people discussed the blog posts I wrote.

 

  • Learn technical skills. Knowing the R programming language means I have a marketable skill. Especially in data-related fields, being fluent in a programming language (such as R, SQL, or Python) is tremendously useful to employers. Online classes are common, but obviously none of these can match someone who has graduate training in a technical skill. Plus, learning technical skills is fun.

 

  • Find work outside of being an instructor or teaching assistant. Academic work is different from non-academic work; finding opportunities outside of academia will help you both (a) figure out if you are interested more in non-academic work and (b) show future employers that you can do non-academic work, if you do choose to take that path.

 

  • Refocus on skills and competencies, not accomplishments. If you are planning to get a job outside of the academy, let go of listing accomplishments. The 9 publications I had in graduate school ended up being one line on my resume. Think about marketing yourself less based on what you have done and more based on how you provide value to an employer. What problems can you solve? What responsibilities can you manage? Even things graduate students take for granted—like being able to efficiently search an academic database—might be important skills that someone cares about far more than the prestigious journal in which you’ve been published.

 

  • Listen to yourself. Think about what you like and dislike doing. Think about career choices you could make that maximizes doing the things you like and minimizes doing the things you dislike. Also consider your personal life goals (having a family, traveling, living in a specific place, etc.) and how different career paths align with those goals.

 

  • And try to tune out others. There are traditional, so-called “right ways” of doing things as a doctorate student (i.e., you do this, then get a post-doc, then a tenure-track position, then maybe move to a better institution, then work toward tenure, etc.), and there are pressures to stay on that career path. But the world (and job market) is changing, and you don’t have to necessarily take that path.

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