Become a Successful Statistician in a Collaborative Research Environment

Aiyi Liu is a senior investigator who has been a member of the Biostatistics and Bioinformatics Branch of the Eunice Kennedy Shriver National Institute of Child Health and Human Development since 2002. He earned his doctorate from the University of Rochester.

Statistics is perhaps one of the few professions to see steady job growth in the past 30 years or so, and the demand for statisticians continues to grow, partly because of the data science initiative.

Due to the applied nature of statistics, graduate students often find themselves landing a job in a highly collaborative research environment (e.g., medicine, public health) that requires not only good training in statistics, but also a fair understanding of subject matter and, perhaps more important, the skills needed to collaborate as a team member with nonstatisticians. Most likely, these important skills are not taught in classrooms, which could potentially hinder the career growth of a statistician in such an environment.

I earned a bachelor of science in mathematics and a master of science in mathematical statistics from the department of mathematics at the University of Science and Technology of China, one of the most prestigious universities there. At that time, I saw no apparent distinction between mathematics and statistics, viewing the latter as just one branch of the former.

Only after I became a graduate student in the department of statistics at the University of Rochester in 1993 did my view and understanding of statistics change. I had the good fortune to work on my dissertation under the supervision of the late W. Jack Hall, who was then the principal statistician on the Multicenter Automatic Defibrillator Implantation Trial (MADIT). Considered by many to be a mathematical statistician, he was studying whether prophylactic therapy with an implanted cardioverter-defibrillator, as compared to conventional medical therapy, would improve survival in high-risk patients. Many statistical problems arose from that trial; some were mathematically challenging. With such a clear connection between statistics and real-world applications, I felt motivated and energized, completing my doctoral dissertation and addressing and answering a number of problems. The unsolved problems later became my research focus for many years.

Over the years, many PhD students have asked me for advice about choosing an adviser and thesis topic. I often share with them my experiences from the University of Rochester. Worth considering are the extent of self-interest in the topic, the ability to solve the problems (What skills might be needed to solve the problems? Are the problems too easy or too difficult to solve?), the research portfolio and personality of the professor (Has the professor been an active researcher in the area? Is the professor a role model for you to follow as both a researcher and future colleague?), and the continuity of research stemming from the thesis (Will this work extrapolate for at least a few years after graduation?).

The job of a biostatistician in biomedical research is not just data analysis. It involves all aspects of the study—study design, implementation, interim monitoring of data quality and study compliance, data analysis and interpretation, and manuscript preparation. Even during the very early stage of conceptualization, statisticians can make substantial contributions by helping the biomedical investigators formulate and refine their scientific questions.

All these study components require communication skills, a fair knowledge of the subject matter (e.g., breast cancer, genetics, and human reproduction), a good personality, people skills, and the ability to bridge statistical and biomedical topics in addition to a profound understanding of various statistical topics. While many of these skills will not be learned in the classroom, some programs do provide courses/workshops on statistical consulting, building collaborations, or communicating effectively. I strongly encourage graduate students to explore opportunities that will help them become better scientists and collaborators.


This essay first appeared on the University of Rochester’s URBEST blog. BEST (Broadening Experiences in Scientific Training) is an effort by 17 institutions to explore ways of improving biomedical career development.