A post-doc student contacted me to know more about the difference between academia and industry (in software engineering). That was an interesting converstation. I tried to summarize the main points as I see them:
Day to day work – In academia, you are more alone and focused on one thing for a long streak of time. In industry, chances are that you will work either in a team, working on the stories in your sprint, or outside of a team, in the role of a faciliator/coordinator, switching between many smaller tasks.
Collaboration – In academia, your main contact is your supervisor, then the peers in your group and some peers in some external groups you collaborate with. In industry, your main peers are your teammates, plus all the external stakeholder you must occasionnaly synchronize with.
Skills – In academia, you focus on conceptional work and data analysis. The engineering part comes second (the code of my prototypes wouldn’t pass a code review). In industry, engineering comes first. Software must have a high quality if it’s developped collaboratively. Conceptual work and data analysis of course also happen in industry, just not all the time (except of course, if you’re a data scientist!). There are plenty of conceptional challenges in industry, but the engineering part will take even a more significant bulk of the time than in academia. In both case, academia and industry require a high attention to details—but other details.
Writing – In academia, part of your job is to sell your ideas in the form of papers. This takes a significant time. In industry, writing is less important, unless you work in a role like facilitator/coordinator. Writing skills are still an asset in industry because you will occasionally have to sell you ideas to your collegue too, and writing can help.
Gratification – In academia, results come after many month of work on prototypes or data gathering. The work might get a few citations and resonate with a few people, but the impact is usually limited, except of course, if you’re very tallented and are lucky to do a breakthrough somewhere. Research brings fruits in the long term, through the accumulation of contributions that enrich the body of knowledge. In industry, the result is more immediate. Broadly, you can work either on software products, where your users are end-users, or plattforms, where your users are other teams. Stories and features are shipped regularly and users will use them. Significant impact for your company can be achieved with ambitious projects over a few years. Impact reaching beyond your company is quite rare, like breakthrough in research.
Engagement – In academia, you pick your topics yourself, so this tend to engages you. On the other hand, if you are stuck with your research and don’t progress much, it might be boring if not depressing. In industry, you have less autonomy to do whaterever you want, but in a good company, everybody should have it’s share of challenge and stay engaged. Software products have different phases: incubating, expanding, maintaining, or decommissioning software all have their challanges. A good company takes care to match the expertise of the team to the challange at hand so that people feel they learn something. There are also many types of software, from application developped solo over a couple of weeks, to multi-year, multi-teams systems. I like large-scale software but some prefer smaller systems where you can iterate faster.
Growth – In academia, growing mostly means getting tenure, which requires a substantial committment over a long time and a bit of luck. In industry, you can reach senior positions relatively quickly if you’re talented. In both cases you can switch course over time, say to reoriented your research interests or technologies/industries that you focus on. Many companies provide dual ladders that can help you grow on the technical side or on the management side. Nowadays ther are many opportunities for leadership besides the traditional “manager” role.
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