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
Richard Morris is a statistician at SRA International, Inc., with more than 30 years of experience in statistical consulting. He earned a BS in zoology from San Diego State University and an MS from North Carolina State University. After working for 15 years in a CRO environment, he earned a PhD in biomathematics from North Carolina State University.

Much of my early career was spent as a master’s-level statistician in a contract research organization. I provided statistical and programming support to researchers at the National Institute of Environmental Health Sciences. I feel fortunate to have worked in this environment because I was able to combine my training in biology, ranging from evolutionary concepts to molecular interactions, with my interest in statistics, driven by an interest in interpreting real data. I used to think I just got lucky. But, I now think opportunity and preparation intersected in a way I simply did not appreciate until later in life.

Today, I believe the opportunity for a life-changing intersection between statistics and, for want of a better term, world knowledge is enormously greater than when I began my career. Technological advances in the rate data are acquired and recognition of the value of integrating diverse data types offer previously nonexistent contexts for developing statistical careers that touch important world problems.

Those of you who are just beginning to choose how you want your statistical training to affect the world have many emerging opportunities. I encourage you, as I did not, to think broadly about which data analysis frameworks and workplace environments you would like to find yourself in as a statistician. The emergence of Big Data brings with it a notable increase in the range of interdisciplinary collaborations. In business, medicine, and politics, a large and complex data landscape is enabling new avenues of applications of statistics and, with them, new opportunities for cross-disciplinary collaboration. What choices will you make?

If I were starting over, I would consider working in a Big Data setting. Increases in the scale and diversity of data now drive many new statistical opportunities in multidisciplinary projects. The acquisition of Big Data, made possible by technological advances in instrumentation, is largely driven by compelling, complex, and important questions that Big Data may help answer. The automated acquisition of remote sensing data has steadily increased in recent years, along with better descriptions of global geological and climatological dynamics. Increased resolution of non-invasive, medical imaging technologies, enabled by new instrumentation and computing techniques, offers new avenues in brain research. Knowledge of consumer behavior has benefitted from mining huge, electronically acquired data sets for interpretable patterns of economic transactions. And the level of molecular description available for medical translation to clinical settings brought about by genomic, proteomic, and metabolomic data is unprecedented. These and other Big Data settings provide new challenges and opportunities for a statistical career. If I were starting over, I would consider one of them.

Not only has the scale of data changed since I began my career, but the culture in which statisticians work also has changed. Efforts to integrate related, but loosely associated, disciplines such as chemistry, computer science, and clinical medicine are motivated by the challenge of solving big problems that transcend traditional discipline boundaries. Such problems and the teams formed to tackle them have contributed to an increase in the diversity of scientific cultures that productively interact. Choosing to join one of these collaborative settings places a premium on interpersonal skills necessary to communicate effectively among different participants. I believe statisticians have a unique opportunity to foster productive communication among team members. Although you may be asked to work on a specific set of activities on behalf of the group, these activities need not limit your contributions to the group. Ideas that cross discipline boundaries often arise from thoughtful statistical input to group goals. An effective collaborative environment recognizes contributions from each of its members and finds ways to apply them to questions of importance. To efficiently work in such an environment, you will need to bring not only the intellectual discipline that is part of your statistical training, but also a belief that team work is essential to solving important problems. If I were starting over, I would consider working in an integrated team environment.

But, of course, I won’t be starting over. Nevertheless, it is a perspective worth spending a moment on since you have choices to make. A career in statistics makes choices available to you. What kinds of problems will you choose to work on? In what setting will you pursue those problems? How will you use your training to improve the world? How you go about answering these questions is important because someday you may ask yourself, “If I were starting over …”