A few years back, working remotely was a sci-fi idea, something people would look forward to in that futuristic society that they would one day inhabit. Over the years, due to certain technological advents, this remote possibility has turned into a tangible option for many jobs, include those related to data science.
Working remotely is also something that many companies (especially start-ups) offer as part of their package deal to new hires. Because, who doesn't want to avoid the commute a few days a month and focus on what’s important in their work? Besides, unless you are a PM or something, you probably don’t need to be in the office all the time to attend meetings and other responsibilities that require your physical presence. In fact, with modern collaboration systems (e.g. Slack) and real-time communication platforms (e.g. Zoom), even meetings can take place virtually. So, what’s stopping people from embracing the remote work possibility full-time? Well, there are several factors, but they all boil down to two things: trust and efficiency. Most companies don’t trust their employees enough to grant them the remote working option for every day of the week. Also, the belief that people work better (more efficiently) when they are in physical proximity, is one that’s hard to shake from people’s minds. These ideas are valid to some extent, so before blaming the companies for not allowing you to do your data science work in the comfort of your home (or local coffee shop), better take a look at the other side of this partnership.
Most information workers today may have the technical skills they need for their work but they may be lacking when it comes to other skills that are essential for being able to work remotely in an efficient manner. Namely, things like self-discipline, good communication, adaptability, are not that common as you would expect. Also, not everyone is able to organize his work when on his own. Still, there are plenty of people who have all these qualities (I've worked with quite a few myself), so they are not something unfathomable. If you think about it, every PhD student cultivates these skills during her project. Meetings with advisors / supervisors may take place in a physical location but most of the time you are on your own, often times during time periods when others are resting. So, being able to work remotely basically boils down to having a strong sense of responsibility and self-leadership.
Having the option of working remotely in a data science setting makes sense for other reasons too. Typically, a data scientist liaises with a small number of people in the organization and unless they are new to the role, they know how to carry a conversation quite well and convey all the relevant information succinctly and effectively. Not every data scientist is an orator, but even the less social ones know how to communicate well, to various kinds of audiences. So, physical presence is not really a requirement for this.
Also, with most of the data science work taking place in a remote location anyway (e.g. the cloud), a data scientist can manage even if he is at home, as long as he has a secure connection setup (e.g. a VPN). So, physical presence in the company is again more of an optional thing, rather than a necessity.
Finally, day-to-day data science doesn't need a lot of resources, other than access to a computer cluster, usually in the cloud, and a fairly decent computer, so being inside a company building doesn't make things easier always. In fact, all the distractions and space limitations may make the whole matter more difficult for the employee, not to mention yield an additional cost for the company.
Perhaps sharing the same physical location with your co-workers has its advantages that no VoIP system can offer. However, making physical presence a requirement, rather than just an option, is an antiquated practice that is bound to give way to more practical and more preferable possibilities in the future, such as full-time remote working.
Zacharias Voulgaris, PhD
Passionate data scientist with a foxy approach to technology, particularly related to A.I.