In the early Spring of 2017, as I hit the 1.5-year mark in my postdoctoral fellowship, I directly compared research career paths in industry and academia and decided to pursue a career in industry. Many times since then, I have been asked about the differences between industry and academia and how I landed an industry job. Perhaps you too are curious about these two different career paths. If so, this was written for you – read on!
Prior to Spring 2017, I had intended on pursuing an academic career. I was well-positioned with a strong publication record, a track-record of successful funding applications, and a supportive network of advisors and peers. In fact, I had never questioned my academic trajectory – I adored research, I enjoyed writing, and I believed in the importance of research for society. However, when I began to prepare myself to apply for tenure-track jobs in the upcoming fall cycle, something felt wrong. My spidey sense was tingling, and so I decided to entertain it to ensure that I was making the right decision at a pivotal moment in my career.
There are many different reasons why one might consider a research career in industry, and these will differ across people. But, for myself, I knew that 3 things were important. First, I wanted clear applications for my research; I wanted my work to have impact beyond a scientific journal (even though I enjoyed writing and believed in the importance of documenting scientific discoveries) and to see it directly improve people’s lives in my lifetime. Second, I wanted flexibility in my life and career; I wanted to have some choice in where I would live (I had vowed to never subject myself to another Ontario winter!), and I wanted the option to easily move around if I lost engagement in my work and/or got excited by a new challenge. And third, I wanted many options and a high ceiling when it came to authoring my career; I wanted the ability to push myself toward different functions (e.g., managing, strategy, and leadership) as I grew my skill set.
I wasn’t convinced that academia could offer me these three things and so I decided to do some exploration. This led me to attend talks and panels, explore various job options, and carry out many informational interviews with both academics and industry researchers. All of the data I collected eventually led me to apply for and land a research job in tech, where I began my industry career focused on cognitive modeling for futuristic wearable augmented reality systems.
(Note that my training is in cognitive neuroscience and my current work is in the tech industry, but the information shared here is fairly generic to research careers.)
Differences between Research Careers in Academia and Industry
At a high-level, there are several key differences between research careers in academia and industry.
Career Path Diversity and Flexibility
For starters, if you’re reading this article, you probably know the academic path fairly well. In fact, academia has a relatively prescribed career path — you start as an assistant professor, ideally in a reputable research institution; you mentor, publish, attend conferences, repeat; you build a strong CV; you hopefully land tenure; and all the while, you maintain a balance of research, teaching, and service. This is likely a very known path to you. Perhaps the only unexpected change when moving into an academic position will be the necessity for strong mentorship skills – your ability to be successful will depend on your ability to train and bring the best out of your students and postdocs. If you are not passionate about mentoring and teaching, you will likely struggle to build a thriving and productive lab. (Note that there are other research roles in the academic setting that do not require mentoring and teaching, such as staff scientist, but these roles are much rarer and are often contingent on grant funding, making them fairly unstable long-term career paths).
Industry, on the other hand, has a less prescribed, more diverse set of paths, and these paths are probably less familiar to you, as they were to me when I was a PhD student.
When it comes to research careers in industry, the most common path is the route of individual contributor. On this path, you focus heavily on research, leveraging your deep expertise to define and solve novel problems. These research roles can be found both in R&D organizations (e.g., research scientist, research engineer) as well as in product organizations (e.g., quantitative UX researcher, data scientist). In many large companies, individual contributor roles have different “levels”, allowing researchers to progress their careers, obtain promotions, and become deep technical experts in their field. Advancing levels involves defining and solving harder, more complex problems and, at the highest levels, being among a handful of experts in the world on your subject matter. The job responsibilities can vary considerably, especially across R&D versus product orgs, but in both cases, it is important to note that in industry you could spend your entire career just doing research. Unlike academia, these roles do not require teaching or management of people, although they often require collaboration, communication, and planning (e.g., roadmapping).
Another career path in industry is the route of research management. This is a group of roles to grow into from an individual contributor role, if you desire. On this path, one focuses on maintaining a research vision/mission and building a well-functioning, productive research team to execute on that vision. Oftentimes, in industry research orgs, teams are organized around functional disciplines. For example, a functional team of UX researchers or machine learning scientists might contribute to several different research efforts. The manager is responsible for understanding the larger problem space, prioritizing high-value problems, helping direct reports align to impactful projects, fostering the career growth of the reports on the team, and advocating for the team’s work. Some companies have a manager role called a technical research manager (TLM). The exact description of a TLM can vary from organization to organization, but typically this role is an entry-level management position that involves managing a small team (1-4 people) with a deeper focus on the technical aspects of the work. This role can allow a research scientist to test the waters of management to determine whether they would like to transition permanently into full-time managment. Eventually, on a management track, one might progress to a Director role or become a Chief Science Officer.
A third path involves program management; those with a research background and with technical expertise might be called technical program managers (TPM). In this role, the TPM owns facilitating the success of a particular research area or domain, although they are not directly managing the researchers on the team. The tasks carried out by a TPM are incredibly diverse and this individual must be capable of wearing many hats. A strong TPM has a broad and flexible skill set, including interpersonal problem solving, process development, cross-disciplinary communication, roadmap maintenance, progress tracking, reporting, and so on. This is a Swiss Army knife role – the TPM needs many key skills and will deploy each at different times. A necessary skill is the ability to work closely with many different types of people to help progress the portfolio of work. There are again career “levels” in a TPM role, allowing the program manager to work at higher levels of team process in an organization and to manage junior PMs/TPMs.
These three different types of industry jobs can look very different across different companies and even different teams within the same company. This is partly due to the fact that a good company tries to foster and play to the strengths of each individual, allowing the employee to bend their work to maximize time spent on the things they’re good at. Ultimately, the diversity of the jobs and roles in industry can be a pro and a con — it allows one to have a lot of flexibility and movement in their career, but it also makes it more difficult to understand exactly what a role might look like and whether it would be a good fit.
Research Direction and Research Funding
The method for obtaining endorsement and resourcing for your research in industry will depend heavily on your company. Unlike academia, you probably won’t have to write long grant proposals to obtain research funding from your company (but in the case of a non-profit research organization, you might have to write grant proposals for the company). Instead, you would likely be hired into a resourced lab, which posted the job ad because research funding and salary had already been budgeted internally.
The question then is, how projects are defined? In academia, you write a grant to propose studies and then you are granted a large sum of money for all resourcing; you may or may not carry out the proposed studies. In industry, your lab can function in many different ways: (1) it might have a defined set of projects for researchers to carry out (top-down); (2) it might have a defined vision that researchers use to define and develop their projects (top-down-bottom-up); or (3) it might require you to set a vision and define projects (bottom-up; this is most likely to be the case for senior roles, start-ups, or a young R&D department). Some companies, especially ones that encourage top-down-bottom-up project definition, will have a light-weight process for pitching new projects. To start a specific project, you might have to write a proposal targeting an interdisciplinary audience or present the pitch to team leadership.
If interviewing for a research role, it is worthwhile to ask how projects are defined and resourced to gauge how much autonomy you will have in direction-setting. For example, a Research Scientist role might focus more heavily on defining novel scientific questions (top-down-bottom-up), whereas a Research Engineer role might focus on implementation of studies and producing results (top-down). The duties associated with different job titles will vary from company to company, so ask anew each time.
Pace and Deadlines
The pace of industry research will vary based on whether you’re in an R&D org versus a product org, but in both cases, it is likely much faster than academia. In product research roles, your research could determine important decisions for the product and as such you might have tight timelines that you must execute against. In R&D roles, you might have roadmaps that guide your project timelines. These are less strict than product timelines because there are fewer dependencies, but they are in place to encourage focus on and prioritization of the most important research problems.
Although the pace is much faster, your work might also move faster because the work is more collaborative. Rather than building all of the pieces of the research from start to finish, you might work with software engineers or hardware engineers to build experimental materials, and you might collaborate with a user study team to complete large-scale data collection.
Collaboration and Interdisciplinary Teams
In many industry research teams, your work will likely be more collaborative both within and across disciplines. For example, you might work with other similarly skilled research scientists on a large project, which could involve working in shared code bases and/or building off of others’ projects or datasets. You might also work closely with experts from other disciplines to collectively decide on the optimal decisions for a project. For example, if you were building a brain-computer interface prototype, as a neuroscientist you might collaborate with mechanical and electrical engineers to understand the hardware limitations and to educate them on the neuroscience needs; you would work closely to arrive at the best possible decisions given the tradeoffs across the disciplines.
These collaborations can be extremely rewarding because together you are able to do something that you could never do alone. Indeed, cross-functional research is necessary to solve some of the world’s hardest problems. However, they require patience, clear communication, and willingness to engage in the full problem space.
Tenure and Market Forces
There is no equivalent to tenure in industry. Academic tenure provides unmatched job security. If and once you obtain tenure, you are very unlikely to be laid off. However, to achieve tenure, you must use your first several years to achieve the requirements, which are not achieved by all.
In industry, on the other hand, most roles are “at-will employment”, meaning the company has the right to lay off employees if their business strategy or financial situation changes, even if the employee’s performance is strong. In a competitive industry, it is likely the case that one can find another job quickly (e.g., I know data scientists who have landed another job within weeks following a company layoff). However, given the lack of a tenure-equivalent in an industry career, it is worth highlighting two factors that have an effect on employability.
The first factor that affects employability is demand for your expertise. This is determined by the current direction of industry research and the “supply” of qualified researchers like you. For example, demand for qualified data scientists has exploded in recent years, making this an extremely profitable career. Starting salaries are very generous and tech companies offer incredible perks to their workers (catering 3 meals a day, gyms, yoga classes, stunning office facilities, etc.). However, what might this field look like in 10 years? Demand might grow, supply might grow, or perhaps some innovation will disrupt the field entirely. These shifts will probably happen gradually, and so in an industry career it is wise to assess the market trends and update your skill set as needed.
A second factor is the economy. During periods of economic instability for the country and/or the company, large companies might cut back on research and start-ups might go under. Most well-run companies will recognize that research investments are necessary for the long-term health of the company (and that layoffs have a negative effect on employee morale), but some companies might not have the option to invest strategically. You can counter this risk by keeping an eye on market trends and the health of your employer to anticipate and respond to shifting priorities, but there will always remain some amount of uncertainty.
Salaries are always changing and are negotiable, both in industry and academia. However, current data suggest that industry salaries in STEM are much higher than academic salaries for entry roles and have a much higher ceiling (especially if one becomes a director, VP, or CSO). That said, a well-positioned researcher can build financial security in both tracks.
If you are unfamiliar with salary prospects in your field, it can be informative to explore salaries for professionals in your field on Glassdoor. Don’t forget that many industry jobs also come with annual bonuses and restricted stock units, which can result in a total compensation well beyond your annual salary (sometimes doubling it or more).
Application of Research and Scale of Impact
A novel scientific breakthrough in academia can change the world, but basic-science discoveries are not typically funded by industry. For example, it is unlikely that grid cells would have been discovered in an industry lab, and yet O’Keefe, Moser, and Moser’s discovery and documentation of our neural GPS has changed the way most neuroscientists think about the neural processing. This type of fundamental discovery has broad impact on scientific knowledge. However, the gap between the scientific discovery and its direct application to the betterment of people’s lives is quite large.
On the other hand, industry research targets a core challenge for people; the goal is not to develop fundamental knowledge of systems, but to solve a problem and improve people’s lives (and, in the case of product research, to eventually turn a profit). It might be necessary to develop new knowledge in industry, but it is done for the purpose of solving a specific problem (e.g., creating a new drug). Even if the projected intersection point between your research and potential product is 5-10 years into the future, the research centers on solving a well-articulated problem for people. This means that the step between the research and the deployment of the work to people is much smaller. Within your lifetime, you could see your research improve the lives of millions or even billions of people.
There are several key differences between research careers in academia and industry. None of these differences are objectively better or worse in industry versus academia. However, it is useful to understand which features are important to you and then use that insight to inform your career decisions.
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