This column is written for statisticians with master’s degrees and highlights areas of employment that will benefit statisticians at the master’s level. Comments and suggestions should be sent to Megan Murphy, Amstat News managing editor, at megan@amstat.org.

Contributing Editor
For the past eight years, Natalie Cheung Hall has worked as a computational statistician at Eli Lilly and Company. She is part of the newly formed Science Driven Adaptive Program group in their Global Statistical Sciences Organization. She earned her master’s in biostatistics from The University of North Carolina at Chapel Hill in 2003 and remains a rabid UNC Tarheel fan despite her Midwest address. 

As an undergraduate at The University of North Carolina at Chapel Hill, I vacillated between becoming a chemistry major and a mathematics major. By sophomore year, it was decision time. I could not see myself working in a chemistry lab, but I also did not see myself teaching math at a local high school. Uncertainty eventually became my livelihood, but at the time, it was a nagging tagalong.

One fateful day in combinatorics class, a flier was passed out that serendipitously asked, “Do you like math? Do you like science? Do you want to explore a new degree?” I answered emphatically, “Yes, all around!” The solution to my major equation was a bachelor’s of public health (BSPH) in biostatistics. Count me in!

After finishing my BSPH, I worked at a contract research organization for a year and a half, but I yearned for a deeper statistical understanding of the projects I was assigned. Thus, I went to graduate school. I found an MPH in biostatistics to be very manageable with high rewards. I say very manageable because it entailed two years of courses, some comprehensive exams, and a thesis, which are all very doable if you enjoy statistics. I say high rewards because I found a plethora of jobs in the market specifically asking for a master’s in statistics. I was practically guaranteed a job after graduation. I don’t know of many other disciplines where this is true for someone with a master’s degree.

The last decision to make was whether to earn a PhD. I was drawn to more practical applications of statistics, and I was ready to get my hands dirty in the data. Eleven years after graduate school, I am now a computational statistician at Eli Lilly. I can say getting my master’s was the right choice for me. However, in a world of mostly PhD-educated colleagues, there are a few tips I keep in mind to stay successfully engaged and happy in my career. They might seem obvious, but as I progress, I find it helps to come back to the following three points:

1. You are a statistician.

A key to being a successful master’s-level statistician is to remember you are always a statistician. A lot of programming positions are staffed by employees with IT or computer science degrees. While it is important to have the mathematical and computing background, it’s your comprehension of the statistical theory behind the analysis that sets you apart. Your statistical background will provide you with an understanding of appropriate methodology, which leads to correct interpretation of the data and differentiates between spurious relationships and statistically significant findings. You are also the person who will notice an issue with the programming or coding, because the results don’t look quite right. You will be the go-to person when it is time to communicate results to nonstatisticians in an accessible way. As the member of a study team that understands both what results to report and how to report them, you are in a unique position to suggest new and better ways to visualize the data. This is a hugely impactful way to contribute to research, as graphs and tables are what people look to for information first.

In my experience, our study team was implementing an adaptive design, which was new to all of us. We were simulating different clinical trials to find the optimal design. My task was to collate the results and present pre-defined tables. However, as I was able to learn more about trial simulations and the statistics involved, I designed more informative tables and graphs that conveyed the pertinent information more clearly and standardized the process for future teams. I did this not in the capacity of a programmer, but as a statistician.

Don’t let your title or degree limit you. Whether you are leading a study or are in charge of coding the tables and graphs, you are a statistician and will always contribute significantly in that role to the benefit of your colleagues.

2. Continue to learn.

In the 2+ years after you earn your master’s, you are working on your career. However, being out of the classroom does not mean you are not still a student of statistics and your professional industry. Wherever you work, innovation is necessary for statistics to continue to grow.

In the pharmaceutical industry, innovation in statistics is vital to bringing effective and safe treatments to patients faster. We need to improve constantly. Being a master’s-level statistician places you at an advantage in this area. With a strong foundation in statistics, and time and experience within your field of work, you will be the innovative force necessary for your department. There are always new areas of interest in statistics that you can explore further. Ask to meet with your local expert. As you gain a deeper understanding, use your knowledge of what your industry needs and apply what you have learned where it is needed. This is the sweet spot: taking the deep theoretical expert knowledge and using it effectively to help on future work projects.

In my role, I worked with a team to help develop simulation software. However, I never had any formal Bayesian training. I worked with Bayesian statisticians and learned the analyses needed to test the software as it was being developed and helped train others in our group to use it. I wasn’t in a classroom with books, chalk, and a professor. However, by leveraging the expertise around me and being open to learning new methodologies, I was able to further my education to the benefit of my department.

3. Find what you love to do. Become an expert. And then share.

Master’s-level positions generally allow a statistician to have more hands-on projects: implementing analyses while working with PhD colleagues. Because of this, we are exposed to a breadth of activities and have the luxury of deciding what areas interest us most. You can become an expert in a certain methodology, programming and coding, standardization of code, automation of statistical processes, trial design simulations, Bayesian computing, high-performance computing, or effectively managing other statisticians. As you find yourself energized in one area, dive deep and become an expert.

We might be intimidated because expert sounds like having a PhD. However, becoming an expert is not just a function of formal education. An expert is someone who has taken the time and energy to understand an area deeply, whether it is the methodology or application of the methodology. An expert also has the agility to navigate new territory, understanding both scientific and operational aspects of an idea. And last, an expert can share learning from his/her experiences.

I think master’s-level statisticians are uniquely equipped to disseminate knowledge, acting as a conduit between scientists who live and breathe the statistics and those who need to actualize the science. I have delved into many of these areas in my career and have found them all interesting. However, the most interesting was being on the team that implemented an adaptive design, and I was able to work on the trial design simulations. This subsequently led to a rewarding position on a team whose focus was providing trial simulation consultation to teams. Today, a large part of my job is passing the skills on to other statisticians. It is fulfilling to play a part in arming our statisticians with the tools and skills needed to run trial simulations.

The career opportunities for someone with a master’s degree in statistics are tremendous. We are in demand in several industries because we bring a strong statistical foundation from our education coupled with the ability to practically execute scientific projects, which leads us to make meaningful contributions at work. Those contributions increase in significance as we continue to grow, learn, and share expertise with others. Reflecting on these key points has been helpful throughout my career. I hope you find them useful, too.