Tag: job market

Looking for a Job? The Three Online Profiles to Build that Can Help Open Doors

(My entire series on how to find a job is here. )

I have spoken with many people who are unemployed looking for a job or want to make a jump from whatever they are doing now to something better. I have already written about how to network with others to find a job, how to strategically prioritize the job application process, and how to nail an interview.

This piece will focus on how to create an online profile that can help you find a job. This two- or three-pronged social media strategy is incredibly helpful in presenting you as an expert professional in whatever field you are working in.

Step 1: Create a LinkedIn profile (and any industry specific online profiles).

Everyone needs a LinkedIn profile, especially when finding a job, so if you don’t have a profile create one. It’s a major hub for job networking in pretty much every industry.

But how to create a LinkedIn profile? For someone who is making a transition to a new field, the best strategy is to position oneself as if one has already done similar work. If you want to continue working in the type of job you’ve always been working in, that’s great; you already demonstrated your experience, but for those who want to make a transition from one field to the other, it can be best to highlight anything you have done that can seem like you have been doing that work or at least something similar.

If you have done something similar in a previous job, even if it’s just one project, blow it up on your LinkedIn description of that job. If you have a job where you can work on a side project that is related to what you want to do, then give it a try. For example, I have advised many who want to become data scientists to incorporate a data science component into whatever they are doing in their current jobs or to create a project at work they do on the side that involves data science. Thus, they can discuss their current work as involving data science work and use that to present themselves as a fledgling data scientist.

People often will describe themselves on LinkedIn as “aspiring data scientist,” “aspiring designer,” etc. Saying you are “aspiring” is the next best alternative if you absolutely cannot describe yourself as already doing it. If you are able to describe yourself as doing it, even if a minor capacity alongside other work, then do it. (And if you don’t have a job at the moment to incorporate a project into, see Step 3.) You will be treated as if your foot is in the door, not as someone knocking and waiting to be let in.

Step 2: Create any industry specific online profiles:

Also, important: if there are specific social medias for the industry you want to work in, create those as well. I’m a data scientist, and for tech, that would be GitHub. In my experience, artists often have an Instagram to showcase your art (frequently a different profile than their personal profile). Video editors often use Fiver. Some industries have specific social media where it all happens. How do you know what profile to create for the industry you want to work in?

Well, if you don’t already know, talk to people in the industry and just ask them (here’s how to network and talk to people in the industry again). They’ll tell you pretty quickly which ones.

Step 3: Work on projects in your new field

This is especially useful for those who are unemployed. Do something in the field. If push comes to shove, work on a personal project in the field, but if you can, do some part-time work helping someone else (networking helps you find these, so see that article; I keep mentioning it because it is important). You can thus list this as work experience. Never mind if it’s only a few hours a week: it’s still work experience. This helps present you as someone already in the field, circumventing the vicious cycle of entry level jobs in a field already requiring having work experience doing it.

For example, ten years ago when I took a data science boot camp during a transition period in between jobs considering becoming a data scientist, I networked extensively to learn about different professions and what people did in the industry. As such, I spoke with someone in charge of a boutique consulting firm who needed a data scientist for a project. I agreed and worked for an hourly wage maybe 4 hours a week on this project. It was actually helpful in itself because it was the first time I did data science in the “real world” outside the simulated environment of my lessons. But I also put it on my LinkedIn as my current experience (the profile didn’t make a distinction between full-time and part-time work experience anyways). My job title on LinkedIn was now officially “data scientist,” and after finishing the bootcamp in several months, I officially had six months experience working as a data scientist. This made me a beginner data scientist in interviews, not an aspiring data scientist, allowing me to circumvent the process of not having had work experience.

The project you work on can be informal. Friends or family may have a project they want you to work on that could fit this. If they officially give you money for your trouble, even if it’s only a few dollars or an ice cream cone, that is even better: on LinkedIn and your resume, it’s now a job. Even a company you started could in theory work. I don’t like the so-called “unpaid internships” because I find them exploitative, but even these could technically work. You don’t have to spend too much time working on them either. Just a few hours a week, and you’re still officially working in the field. People can often fit only a few hours a week into even the busiest schedule.

Step 4: Create a professional website/blog

This can be the most time-consuming but super important step: create a professional website or blog in the subject matter you would like to work in. If it’s data science, make a data science blog that also showcases your data science projects; if your a designer, make a blog about design that also showcases your design work; etc. This will position you as a knowledgeable person contributing ideas in the field.

For example, when I was fresh out of graduate school, looking for a job in data science, I initially created this website (ethno-data.com). My purposes for this blog have evolved overtime, but one of my initial reasons for creating it was to find a job. From that perspective, this website reaped dividends: you’d be surprised at how many people will reach out to me because they have found some of my articles interesting and have a role that might be a good fit for what I do.

Try to pick a specific niche within your field that is interesting to you personally and compelling to at least some others. It doesn’t have to be the most popular idea in the field; more important that it is something you enjoy and feel comfortable nerding out on. You can combine multiple areas of expertise you have, focusing on how they overlap (for me, that was ethnography and data science/AI). Make it reflect you. That both helps sell you and your unique contribution to the field, but it also tends to make it more interesting to you. You won’t want to post on content again and again if it’s not you.

There are so many different platforms to use to build the website (from WordPress to Bluehost to Medium to Squarespace and so on), so do your research on which one works best for you. These platforms tend to walk through the process for non-techies anyways. Set yourself a posting schedule and have at it. Whether you write articles once a week, every other week, every month, matters less. What tends to matter is that you post according to a consistent schedule.

Last but arguably most importantly, mention your articles on LinkedIn and any other social media platform you are using to network. If you post an article in your blog that week, write a statement on LinkedIn (or any other social media you are using) summarizing what you talked about and link to it. This will not only encourage people to read your pieces, but most importantly of all, it will present you online as someone contributing to the field. You are helping to advance your field. As such, people will be more likely to hire you.

Conclusion

That’s it. If you do all four of these, you will have a kick-butt online profile that will help you find a job, even if it’s a profession you are transitioning into. They are all practical things you could set up anytime. With the exception of Step 3, which sometimes requires coordination with others, you can do each of these on your own. You can even get started right now if you want.

Is data science still the sexiest job?

Photo Credit: Mahdis Mousavi

In 2012, this Harvard Business Review article argued that data science will be the sexiest job in the 21st century. At the time, data science was new and unheard of, with companies eager to use data scientists to revolutionize their practices. Is it still the sexiest job now? Well sort of, but not really. The field has gone through some significant transformations since these “wild west” early days. Now, data science as a discipline has become more streamlined and specialized.

Often key data scientists have slightly different titles like machine learning specialist, data engineers, etc. Machine learning and AI technology have changed the way data is processed and analyzed. This has automated parts of the tasks that data scientists have spent a long time working on, such as data cleaning (which still can take a long time) and initial data exploration, shifting the work necessary for humans to perform in the field to more specialized and fringe tasks. For example, many data scientists have become machine learning specialists focusing on fine-tuning these models or communication specialists focusing on how to use their business expertise to communicate complex findings with stakeholders and help decide what they should do about the results.

I think more than the technology, what has driven the specialization is the routinization of data science processes within an organization. Gone are the days of a lone data scientist at a company doing cutting edge work by themselves just figuring out what is possible. Data science as a field has fallen within the discipline and expectations of corporate bureaucracies. In its early stages, most data scientists worked alone or in small teams doing pioneering, experimental work figuring out how to apply the tools of the field to their organization in ways that people did not know were possible. That can still be the case. In every job I have had as a data scientist, for example, I have been the first data scientist in the entire organization or specific department I work in. But this is increasingly rare. Data science is now mostly one department at an organization, doing important but predictable routine work. All white collar professions get grafted in the “corporate machine” like this overtime.

Recent AI technology has contributed to this too by automating many of the low-level data processing and analyzing tasks so that non-specialists can perform them on their own. This is great, increasing the accessibility of tasks once considered obscure or even “magical” by regular people. Back in the day, to do much of any data modeling, you had to code it yourself, requiring a level of programming knowledge that was beyond a typical office worker or manager. That’s why they needed to hire a data scientist to analyze the data themselves. I hope in the long run using AI tools to tinker with data themselves and try out different theories will increase the data literacy and skillsets of regular professionals. It also means that data scientists are increasingly spending less time on these tasks and have moved to more complex, specialized work that still require quite a bit of technical human thinking.

Another factor that has driven this routinization is the increase in the number of people studying and doing data science. As demand for data science increase, more people have tried to become a data scientist, whether by receiving a degree in it or transitioning their careers into the field. This has led to more data scientists in the market. If this trend continues, eventually the field will become oversaturated, but the demand still seems to be higher than the supply, with more open jobs than people able to fill them.

This has still redefined what data science is. When many people join a field, it becomes difficult to maintain the same level of pioneering eclecticism. Instead, the types of tasks people do become routinized and standardized to provide consistency for a larger number of people, paralleling the transformation Max Weber describes religious movements undergoing from a charismatic leader to a routine social institution.

All of this leads to the current state of data science. This is not necessarily bad, but it is different. So, is data science still the sexiest job? Yes and no. Some of its specialist roles like machine learning specialist, I think, better maintain the excitement and cutting edge of that moniker. It’s still in high-demand, however, a fine field to work in.