Meet the data scientist: Alex Herrington
By Stephanie Robertson, SAS Insights Editor
When he was just a teenager in high school statistics, Alex Herrington decided he wanted a career in data. Why? Because he liked the idea of using numbers to figure out why people do the things they do. As part of our Data Scientist Series, we interviewed Alex Herrington, a data scientist for a major US retailer.
What’s your background and education?
Herrington: I got my undergrad degree at California Polytechnic State University San Luis Obispo (Cal Poly) and got a BS in statistics. Then I headed east to attend North Carolina State University for my MS in analytics. Now I’m at my first corporate job with a major US retailer.
What skills help you most as a data scientist?
The most important skills I bring are a data-driven mind that looks to apply data to business problems. I also have a statistics background, which is helpful when trying to mathematically explain what’s happening in the data and why. Some of the most important skills for a data scientist are the ability to process large amounts of data without feeling overwhelmed and then being able to apply the learning to the business.
When did you figure out you wanted to be a data scientist? What motivated you to become one?
Way back in high school I decided I wanted to pursue a career in data science, but back then it was called statistics. My high school statistics course made me realize I wanted to get a BS in statistics because I liked the idea of using numbers to figure out why people do the things they do. I was motivated by the need for statistics in every field, and I liked how I could apply my knowledge to any field of work.
What department do you work in and who do you report to?
I’m in a hybrid finance group that is data driven and I report to a manager in the analytics group.
How long have you had your job and were you hired specifically to be a data scientist?
I’ve been with my current company for more than 1.5 years and was specifically hired to be a data scientist.
Do you work on a team? If so, what's the makeup of your team?
My day-to-day work has me interacting with coworkers who have backgrounds similar to mine. I often work with other members of the business who have finance, marketing, engineering, computer science, and/or social science backgrounds.
What’s your job like? Is there a typical day, or is each day different? Can you give us a basic idea of what you do and the kind of projects you work on?
My days can be very similar but week-to-week work can vary greatly. For a few weeks I might be working on a text mining project, and after that I could be creating a predictive model around the customer. Mixed in are meetings with others about analytics and how it can help different parts of the business.
Is your job what you expected it to be?
My job is much more than I expected. I would not have expected to have my work so highly visible and sought after throughout the company. I also didn’t expect to have so much freedom in the type of analytical methods and tools that I use.
What’s your biggest challenge?
My biggest challenge is knowing that I can’t work on every project. For me to be more successful I need to focus on one or two major areas of the business and become the expert.
What’s your biggest accomplishment thus far?
My biggest accomplishment thus far is how much of an impact I’ve had on analytics at my current company. I’ve had a significant part in deciding what analytical technology is needed and have been able to bring many new analytical techniques to the business.
What do you enjoy doing in your spare time?
One of my favorite things to do is cook. When I am not cooking or working, I enjoy hiking, golfing and rock climbing.
What’s your favorite new technology or app?
My favorite new technology is what is being put into cars. I’m curious to try out some of the automatic braking and lane assistance features.
- Whether you want to find out what data scientists do, hear from some real-life data scientists or learn how to become one, check out all the articles in our Data Scientist Series:
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