Early Career Aspirations
At a young age, Casey lost her mother to cancer. This tragic event influenced Casey’s professional interests and for a long time Casey was sure that she wanted to become an oncologist and cure cancer.
As an undergraduate student at Kalamazoo College — a small liberal arts school in Michigan — Casey focused her studies in math, chemistry, biology and genetics in preparation for medical school. In 2007, Casey graduated with a major in math and chemistry and was ready to pursue her aspirations in cancer research.
At this point, despite having already taken the MCAT, Casey made the decision that medical school was not for her. Naturally an outgoing and caring person, Casey felt that she would have a difficult time, emotionally, working with cancer patients.
While in college, Casey spent her summers working in genetics labs. After college, she decided that getting a full-time job in a genetics lab would combine her passion for cancer research with her quantitative skills. Casey worked in this lab for about two years when she decided — with the help of a mentor at the lab — to go back to school and get a master’s degree in biostatistics from the University of Michigan.
Casey returned to academia with a renewed desire to learn: she wanted to be there, she cared about her homework, and she learned not to procrastinate. In her two years at Michigan, Casey not only developed a strong foundation in statistics, but was lucky enough to be exposed to the R programming language, using it to solve complex problems with data in extracurricular research projects.
After graduating, Casey moved to Seattle, where she worked for several years as a statistician at The Fred Hutchinson Cancer Research Center. In 2013, the field of data science was quickly growing and Casey decided to consider jobs outside of the healthcare sphere in order to broaden her skill set.
Making the Jump to Data Science
Casey applied to as many openings in the Seattle area as she could find, and had the opportunity to interview for a small fraction of those. A relatively small company named Revolution Analytics took a chance and offered her a full-time job as a data scientist. Despite feeling unprepared for the position, Casey jumped head-first into this new role and began her career in data science.
Two years into her tenure at Revolution, the company was acquired by Microsoft and her team moved over to the tech giant. Casey’s time at Revolution and Microsoft proved to be invaluable, giving her extensive experience with software development and distributed computing. Toward the end of her tenure there, she was eager to take the skills she had acquired and apply them to real-world data problems.
Casey applied to PayScale at the suggestion of a fellow Kalamazoo alum working in sales at the company. She was drawn to Payscale’s interesting data sets and their mission. She was offered a position on PayScale’s rapidly growing data science team and has now been with the company for just over a year.
Advice From Casey
Whenever Casey switched jobs, she says that she tended to feel impatient, because she wanted to immediately be the best in the field. But in reality, learning new skills and technologies takes time. Casey’s advice: “Be patient, it’s going to work out no matter what. Give yourself the freedom to explore what you really want to do and don’t be afraid to do it on your own.”
When Casey made the switch from research to data science, she felt a bit intimidated by the lengthy list of qualifications needed for many data scientist positions. She has found that while it’s fine to not know something, it’s also good to learn a little bit about what you don’t know: “Having a better understanding of what you don’t know is great and it hopefully gives you confidence to own what you don’t know.” Similarly, don’t downplay the experience that you do have!
With several years’ experience as a data scientist, Casey has now had the opportunity be on the other side of job interviews. Casey knows firsthand that it is impossible to be an expert in every technology — especially as a new college graduate. Instead of just testing candidates on what they know, Casey really values seeing how candidates think. She advises would-be data scientists to be able to explain what they did on a project, but also be able to explain why they did what they did or what they would have done differently.
Many people at crossroads in their careers stress out about making the right decision. Casey believes that there is no “correct” path and that “a smart person could choose multiple different paths and be really good at it.”Casey's advice: 'Give yourself the freedom to explore ... and don’t be afraid to do it on your own.'Click To Tweet
Casey’s path suggests that following your interests and leveraging the skills you pick up along the way is a successful strategy, even if the course isn’t straight and narrow.
Knowing that I do not want to pursue a higher education in physics, I am always thinking about how I can use the quantitative skills I’ve developed at Santa Clara in other fields. Because of this, I found it very interesting to hear how Casey made the switch from research in healthcare to data science.
Casey pointed out that people in data science come from all sorts of backgrounds. Some come from a statistics and analytics background, but others come from a background in science or computer engineering. Making sense of massive amounts of data takes a unique blend of quantitative skill and creativity; and having people from different fields is a great way to bring diverse perspective to a team.
I also think Casey’s advice about “knowing what you don’t know” is extremely valuable for students in all industries. These days, even entry-level job openings often require qualifications that no new grad will have. Learning a bit about what you don’t know might be the trick to land you your first job out of college.
Be on the lookout for next week’s post featuring Amanda Powter, VP of Technology & Product at Oath.
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