Academics are surrounded by smart, hard-working and highly skilled peers who can wax poetic on complex statistical procedures and cite authors of obscure theories. Thus, a typical graduate student or postdoc who has been submerged in such an environment occasionally wonders “When everyone else is so highly accomplished, what can I offer to an employer?” Yet, many skills that academics take for granted are highly sought after and admired in industry. Here are some examples:
Researchers solve problems and through extensive training know how to look for and find answers to questions that have never been asked before. The toolkit of a researcher contains various methods for finding answers: collecting qualitative and quantitative data through experimental, observational, ethnographic, historical etc. research. For someone coming from a strong quantitative (or strong qualitative) research background I do recommend gaining at least passing familiarity with the less practiced data type collection and analysis methods.
Researchers think critically about their own process and can evaluate others’ attempts at finding answers. Researchers understand the value of and practice rigorous scientific method. Exposure to statistics has ingrained the concept of illusory correlations, that correlation does not equal causation, and other laws of looking at data.
At some point in their careers researchers take a leading role in conducting experiments from start to finish. This covers experience in setting up the studies, recruiting participants, setting up the lab, materials and equipment, developing procedures, executing data collection, supervising research assistants, synthesizing and analyzing data, presenting findings to peers and so on. Just this experience of running a study alone covers a lot of the ground that a researcher in industry (e.g. User Experience or Usability Researcher) engages in on a daily basis.
Working with Data
Descriptive statistics (means, standard deviations, ranges etc.) crunches lots of numbers into a handful. Business stakeholders greatly value such summaries of data.
Inferential statistics for comparing sets of data, for assessing whether a rate of change is significant and so on are very impressive skills that can be used to inform business decisions based on data. Graduate level statistics skills are extremely impressive. Experience with neuroimaging data is an example of experience working with large and complex datasets.
Visualizing data, whether in charts or tables, is an impressive skill for telling stories with just a few numbers.
Excel knowledge is often quite sufficient; skills with SPSS, R, SAS or some other software are impressive but unnecessary unless “Data Scientist” or “Data Analyst” is the sought position.
Communicating in Writing
Academics write many papers which means they have, on average, more practice at communicating in writing than a typical job applicant who is fresh out of college. Good grammar, correct spelling, short and to the point written documents win many brownie points in industry. Researchers are often tasked with providing an executive summary of findings and recommendations for the stakeholders: this is our typical abstract in bullet points and even shorter word count.
Lecturing in a classroom, department brownbags, conference presentations, and even Master’s thesis or dissertation defenses are all examples of public speaking experiences. The verbal presentation of ideas as well as the experience of preparing supporting materials transfer directly to client meeting presentations (of which there are always plenty!), when ideas need to be pitched, or defended.
Teaching is a great example of making complex material accessible to novices and non-experts. In the business world such skills are always handy whether during internal or client meetings.
Working in Teams // Managing Others
These days researchers rarely work in isolation. There is always an advisor, colleagues, graduate students or postdocs, research assistants and various other entities outside of the academic lab that in one form or another influence scientific inquiries. Teamwork in business is critical. Experience in supervising junior researchers (even if they are undergraduate students) help develop management skills that can be valuable as one climbs the career ladder in industry.
Working on Multiple Projects
Researchers often run several experiments in parallel; some may be in the data analysis stage, others in material preparation. Juggling multiple projects and prioritizing daily tasks is an absolute must for a researcher in industry who wants to retain her or his sanity in the busy and tight-deadline world.
Matlab, Python, R, SAS and even scripting in SPSS may not be required or even ever used on the job in industry, but these are impressive (STEM-like, if not STEM-exactly) skills.
These are just a few examples of skills academics possess and which can be greatly utilized in industry. I hope this list kindles inspiration!
Blog is part of series on transitioning to industry from academia