Ok, you’re knees deep in your graduate training in cognitive neuroscience with n years spent honing your study of the 13 ms processing cost that occurs after repetition of some phonemes and not others (where n equals “too many”), and you’re considering what comes next in your career. If – like me – you’ve spent approximately 5 years in the grad-school research trenches trying to understand a fundamental cognitive or neural process, it might be difficult to imagine why any company might actually pay you for those skills.
Well, rest assured, your graduate training likely equipped you with skills that are very valuable in industry. You just have to find the right fit.
The purpose of this essay is to describe some of the types of jobs that are available to people with training in psychology, cognitive science, and/or neuroscience. This post was written to serve as a resource for graduate students and postdocs who are considering the next phase of their research career (disclaimer: this essay is long for that reason).
This list is not comprehensive nor is it perfect, but it will give you rough idea of the landscape and equip you with some job titles and keywords to begin your job search and your invaluable informational interviews. My aim is to equip you with enough information to help you focus on a category of jobs that appeals to you.
Important Differences Related to all Role Classes
Prior to describing different job types, it’s important to note that there are some key differences in jobs that cut across all role classes. Two of these are Product vs. R&D research and Specialist vs. Generalist roles, which I’ve articulated in a separate essay.
Additionally, within each of these role classes, people might pursue an individual contributor role or a manager role. Many companies have a structure path for career growth both as a researcher and as a manager. In other words, management is not a necessary step for career growth. I have discussed the differences between these two tracks in a prior essay on academic and industry research career paths.
Role Classes and Alternate Titles
The following classes of roles can be found in both product and R&D jobs. However, you will find most UX Researchers and Data Scientists in product research and more Research Scientists, Research Engineers, and Technical Program Managers in R&D organizations.
Additionally, it’s worth noting that these job titles are not used consistently across all companies; for example, a “Research Scientist” job might actually align more with the description I’ve provided for Research Engineer. As you progress through the interview loop, be sure to use those opportunities to ask your own questions to determine whether this is in fact the type of job you’re looking for.
User Experience Researcher
UX Researcher jobs can have many different foci, but the UX Researcher role is defined by a central tenet: A UX Researcher’s primary mandate is to understand and advocate for the end user. The end user could be a single customer or it could be a business. In any case, a good UX Researcher must strive to deeply understand the product from the end user’s perspective and then ensure that development choices are made to maximize value for the user.
If left to their own devices, engineers might build technological feats that are completely unappealing to the end user. A well-structured team will integrate UX Researchers into the development cycle to make sure the developed product will actually work for the people it is meant to serve.
Early in the research cycle (i.e., in R&D or in the infancy of a new product), user experience research might involve exploring different value cases. For example, if a user were to own a pair of augmented reality glasses, what would she want to use them for? Who would this technology most appeal to? What new functionality would add the most value to a user’s life? In some cases, these explorations might be carried out without consideration of current technological limitations. For example, current virtual reality headsets have a field-of-view that is smaller than the field-of-view of the human eye. A UX Researcher might explore value propositions that are unconstrained by field-of-view because a strong value proposition can be used to guide research investment to solve novel hardware or software challenges.
Later in the product development cycle, user experience research involves understanding current and potential users of an existing product. The primary goal is to identify pain points or untapped areas of value, and then working with development teams to enhance the product.
Many UX Researcher jobs will require some combination of the following skills: user/participant interviewing, qualitative data analysis, survey design, quantitative data analysis, statistical testing, experimental design, and A/B testing. Empathy and communication are critical given that this role focuses on understanding and advocating for users’ needs. As you grow in this path, strong communication skills and interdisciplinary collaboration will be essential for success because you must advocate for the user and convince others to incorporate team findings into the product development cycle.
Other Job Titles
There are a few variants of UX Researcher roles. A “Quantitative UX Researcher” role primarily involves quantitative data analysis to understand user needs. You might also see jobs with the title “Behavioural Scientist”, where one would focus on understanding human behaviour and apply these insights to a specific product domain. And finally, a “Product Researcher” or a “Usability Researcher” might focus specifically on the usability and design of a particular product; often, these two roles do not require a PhD and can overlap with design research.
The core mission of a Data Scientist is to leverage messy data to drive company strategy and objectives. While UX Researchers are primarily focused on understanding a user through analytics, there are many data science roles that have nothing to do with user data. For example, a Data Scientist might work on the technical performance of computing tools or the effects of an ad campaign on revenues.
Some Data Scientists focus primarily on exploratory data analysis; the research aims to discover new opportunities for improvement in a particular domain. For example, a Data Scientist might be tasked with exploring the accessibility of a product and then use those insights to propose improvements to the product. Other roles might instead focus on well-scoped problems, such as purchasing trends or advertising revenue. The work might focus on building and maintaining data pipelines, making projections, and informing business leaders of progress against key performance indicators (KPIs). In reality, a Data Scientist will likely work on both exploratory research and well-scoped problems, or they might move between them during different phases of the product cycle, and that ratio will vary from job to job.
Some large companies have a “core data science” team with many highly skilled, specialist Data Scientists. These Data Scientists solve particularly difficult or novel problems that cut across many teams within the company. The team might focus on building tooling and models that are generally useful to other Data Scientists (who are embedded instead in product teams).
Many of these jobs will require some combination of the following skills: statistics, computational modeling, machine learning, critical reasoning, hypothesis testing, data mining, building data pipelines, A/B testing, and strong coding ability (e.g., SQL, Python, R, AWS, TensorFlow).
Other Job Titles
A “Machine Learning Scientist” must have deep expertise in ML (e.g., deep learning, NLP, recommender systems) and might drive the research and development of novel algorithms; at the moment, these skills are in huge demand in tech. Similarly, a “Machine Learning Engineer” mixes statistics, engineering, and experimentation; people in these roles tend to be much stronger in coding and engineering than the average Data Scientist. A “Data Engineer” might focus on building and maintaining data pipelines; this role does not necessarily require a PhD and is well suited to those who love coding and managing data rather than data analysis and scientific exploration. Finally, the title “Applied Scientist” can mean many different things, but in some companies, it refers to a scientist who both carries out data science work and writes production-quality code to implement their work in live systems.
A Research Scientist focuses on defining and solving novel scientific problems within their domain of expertise. The primary mandate of a Research Scientist is to scope ambiguous problem spaces and carry out research projects to demonstrate new value. Core activities involve defining new research questions, building short- and long-term research roadmaps, defining research strategy, exploring and analyzing data, testing hypotheses, building prototype systems based on research, and synthesizing findings for distribution (e.g., internal whitepapers, external scientific publications).
The day-to-day activities of this job are not consistent. This job has no clear path because the job is to create the path. As such, strong reasoning skills, creativity, and deep domain knowledge are essential for success. A Research Scientist will also likely work on big problems that extend beyond their discipline, which requires strong cross-functional communication and collaboration. For those who are willing to take on the challenge and work in ambiguity, this job can allow you to invent the future.
The main skill you will need is deep domain expertise. That is, you should be really good at your profession with a demonstrated track record of scientific productivity. For example, if you are applying for a Perceptual Research Scientist role, you should have demonstrated expertise in studying human perception and a resume to support that expertise (e.g., publications, conference papers, a Github account with analysis code, a blog, etc.). You should also be able to develop plans that take theoretical frameworks/findings and extend them to concrete problems; for example, you might create plans for how scientific knowledge of low-level visual perception can solve the perception of flicker in various novel virtual reality displays. In industry, you will also benefit from the ability to communicate and work cross-functionally.
These roles often require a PhD. They are the closest to basic science but will require a mindset shift. Rather than scientific knowledge as the end goal, you must extend beyond that and make a case for how your knowledge discovery work might start new lines of product development, even if that intersection is 2 to 10 years in the future.
Other Job Titles
When searching for this role, you will see the title “Research Scientist” plus a descriptor. For example, you might find roles such as “BCI Research Scientist”, “Perceptual Research Scientist”, “Behavioural Research Scientist”, or “Research Scientist, Machine Learning”.
A Research Engineer focuses on building novel research systems, often in close collaboration with Research Scientists. This is a research role, but the core mandate is to build and refine, and as such, strong engineering skills are required.
In contrast to a Research Scientist (who focuses on identifying problem spaces), Research Engineers work on executing and refining identified research paths. The aim is to design, build, test, analyze, and iterate on novel research concepts. This is a highly rewarding role for those who enjoy research but do not want to own the definition of unsolved problems and creation of research agendas.
These roles require strong technical skills, with some combination of: scientific programming, computer science knowledge, experimental design, data analysis, modeling, statistics, and technical writing. These roles may not require a PhD but might require some related domain knowledge.
Other Job Titles
Similar to the Research Scientist job title, “Research Engineer” can have a variety of added descriptions, but the core title is more or less consistently used.
Technical Program Manager
A Technical Program Manager (TPM) is not a researcher, but they partner closely with researchers to facilitate research programs. Their core mandate is to ensure the health and success of a research program.
TPMs work with researchers to coordinate direction, resourcing, process, and strategy of the research program. This is a great path for people who have decided that research is not for them but who would like to leverage their knowledge of the research process to facilitate industry research programs.
A TPM’s duties will vary considerably based on seniority and industry experience. In a prior essay, I’ve called this a Swiss Army knife role – the TPM needs many key skills and will deploy each at different times. Depending on level, a TPM must construct and track budgets, create and implement team process, facilitate and maintain research roadmaps, identify/anticipate risks for the program (interpersonal, process, or technological), build cross-functional relationships, develop and share the narrative for the program, and so on.
The tasks carried out by a TPM are incredibly diverse. TPMs must be capable of quickly shifting between different needs. As such, the TPM role requires a broad and flexible skill set; some of the most useful skills include strong cross-disciplinary communication, interpersonal problem solving, organization, attention to detail, and reporting and documentation. Of these skills, strong communication and organizational skills are essential. A PhD is not required for this role, but some graduate training and domain expertise can be helpful.
Other Job Titles
This job is similar to “Program Manager”.
Although these classes can help you focus your search on a well-matched domain, you will still find incredible variety within a job class across companies and even within a company. The nature of your work and the skills that you’ll leverage will depend heavily on the company and project. As such, ask many questions to gauge the type of work before you take a role to ensure that it matches with your career “must haves” and that you are set up for success at the new job.
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(Special thanks to data scientist extraordinaire, Laura Libby, for feedback on the Data Scientist track, and to decision science guru, Alex Filipowitz, for feedback on the Research Scientist and Research Engineer tracks).