Research roles in industry come in many shapes and sizes. If you’re searching for a new job in industry research, there are a few important details of various jobs that might not be made explicit in the job description. And yet these differences will be a strong determinant of the types of work you’ll be doing and the types of skills you’ll be able to grow on the job.
These are things you’ll want to get a read on during the interview to determine whether a job prospect fits with your research career “must haves”.
Product versus R&D
There are – very roughly categorized – two types of research jobs in industry: Product research and R&D. This is a crude distinction and there are certainly jobs that sit on the boundary (e.g., “tech transfer” research, exploratory product research), but most jobs will lean towards one type or the other.
Broadly categorized, R&D jobs involving exploring, understanding, and developing novel technologies, concepts, or solutions that do not exist yet. For example, in the space of wearable augmented reality glasses, this might include questions such as: How can you overlay virtual content onto the real world without overwhelming or distracting people? How can you infer the types of information that someone might want in the moment? How can you enable typing using only the brain?
Product jobs, on the other hand, focus on optimizing for known value in a particular space; the aim is to create and refine a target technology or solution. For example, this might involve building a better interface for an existing virtual reality platform or identifying new features in an app that make it more assessable to those with disabilities. Note that some product-focused roles might also focus on nascent product spaces that have not yet been well-defined; here, the research work might inform the development of concrete objectives and strategy for a new product space. These types of roles are similar to R&D roles because they involve exploratory research to provide clarity in a space, but they likely have shorter timelines and a more focused exploration.
There are several other notable differences between these two types. R&D jobs typically require more domain expertise (e.g., expertise in human memory retrieval processes) whereas product jobs draw on core transferable skills (e.g., statistical modeling, experimental design, scientific communication skills, interviewing and qualitative data analysis, etc.). Product jobs tend to be in much greater abundance than R&D jobs, as they are directly related to the short- and mid-term strategic investments by the company. From my survey of the field, R&D jobs tend to center around novel hardware platforms (e.g., novel neuroimaging technology, augmented reality glasses, driverless cars) because these areas require long-term research investments; the jobs themselves might not focus directly on hardware development, but the research areas are part of the full stack for that emerging hardware (e.g., creating a novel interaction model for augmented reality glasses) and they therefore require long-term co-development with hardware. Generally (but not ubiquitously), product jobs move more rapidly, have clearer scope, and have visible and direct impact on people’s lives, whereas R&D jobs involve more ambiguity, more exploration and scoping, and the opportunity to create a paradigm shift in an area.
Specialist versus Generalist
Within each of these role classes, some jobs might require a highly specific skillset – the specialist – and others might target a broad and flexible skillset – the generalist. For example, a company might recruit a UX Researcher who has deep domain expertise in understanding and creating user trust, or the company might want a highly flexible UX Researcher with a broad skillset who can quickly adapt to a variety of different product needs. Similarly, a Data Scientist might have deep expertise in cloud data architectures, or they might have a breadth of knowledge that cuts across data acquisition, data processing, and modeling (for a discussion of these two types in data science, Cameron Warren shares an useful overview and perspective).
Specialist roles might be more likely to be associated with R&D organizations and/or large companies, although they are perhaps in small quantity overall.