Hunting Unicorns: How DataScience@SMU Revamped My Job Search
By: Bryan Hudson, DataScience@SMU student
Bryan Hudson earned an M.S. in experimental psychology from Northeast Louisiana University in 1999, married his college sweetheart and moved to the Dallas-Fort Worth area, where he has worked as the manager of a mental health study, a business consultant/analyst, a computer programmer and developer, database administrator, and data scientist. Bryan is currently enrolled in the DataScience@SMU program.
Searching for a new job is not the most enjoyable of endeavors, but searching for data scientist positions while enrolled in the DataScience@SMU program was a different experience. Having done my share of contract work and job hunting, I had the process pretty much figured out — or I thought I did.
My normal routine is to update my resume and online profiles with all of my new work experience. If I’m between jobs, I change my LinkedIn status to “currently seeking.” I reach out to my network of recruiters and let them know my current search criteria. Then the daily trudge through all of the job boards begins. Friends and family will offer to help and forward postings they think I’m interested in. In the past, most of the job leads came through external recruiting services, so I had to field a bunch of calls from recruiting firms, many for positions I was not remotely interested in. Wading through all the subpar job openings to find an opportunity that came close to my original goals was usually a long and tedious process.
This past December I found out that that my position was being eliminated, and this time my priority was to find employment where I could use at least some of the skills I was learning at SMU. Since I would be eligible for unemployment insurance for six months, I decided that the first two months would be dedicated to finding the elusive ideal job, then next two months would be used hunting for a “good” job, and the last two months would spent finding something I could live with until graduation. I knew that I’d probably have more options once I finished the program and had the degree on my resume.
My list of requirements was, I thought, not overly long or too demanding. It included the type and amount of work, minimal travel, being able to schedule any after-hours work around classes, a reasonable commute and, probably most important to me, being a valued member of a team that valued my opinion. I had heard that the data science field is exploding and how in-demand the skills are, or will be in the near future, but I wasn’t sure how close I could come to finding my ideal job. In my mind, finding a true data scientist position was my own personal “unicorn” hunt. My realistic hope was to find a position where I could occasionally use some of the data science skills I learned in class.
Within the first two weeks of searching, I came to the realization that I had been totally unprepared for the attention I would receive as a DataScience@SMU student. Because it was public knowledge at my previous company that I was leaving, I was able to update my LinkedIn profile to reflect my search status while still being employed. Before I had even left that position, several companies expressed interest in me for genuine data scientist positions.
Not all of these jobs matched my criteria, but the interest was encouraging. When recruiters called about junior-level analyst or mediocre offerings, I would politely explain that I was only interested in data science positions. I had to explain to several recruiters what data science was and how it differed from normal business intelligence analyst positions. If the firm was larger, a recruiter would refer my account to the department that specialized in these positions. If the firm was smaller, which most of them were, offerings were limited to the specific positions it was trying to fill, none of which were even close to data scientist.
Where this job search really differed from previous ones was the number of internal recruiters searching specifically for people with my qualifications. The term “unicorn” has been thrown around to describe that rare combination of skills necessary to be a data scientist: statistical skills, programming ability, business knowledge and being able to effectively communicate with varied audiences. Every week we see rooms full of classmates with these skills, so I didn’t realize how rare we are in the work force. It wasn’t until speaking with a couple of internal recruiters that I began to understand how desperate they are to find qualified data scientists.
Having the “enrolled in SMU Master of Data Science” on my profile resulted in multiple companies actively pursuing me, all for positions not listed on any of the job boards. I’m proud to say that my search ended with a wonderful position where I am encouraged to explore and use my knowledge and data science skills. For any of you who will be looking for new positions before or after graduating, I strongly encourage you to emphasize your enrollment and use it to land your dream job. Instead of searching for the elusive unicorn job, the hunter has become the hunted. We are the unicorns, and the hunt is on for us.
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Last updated May 2016