jobs Archives - Ethno-Data

(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.

Many are concerned that AI will take away their jobs. This is not unreasonable, and one underlying reason why this is a fear is that the current AI technology has been partially built to automate. The corporate world has been trying to automate and mechanize human work for a long time, and in the last several decades, in particular, we have seen the steady routinization of white collar and thinking work. The recent AI technology has developed in this sociocultural context. I think that if this desire for automation wasn’t widespread in our society, recent AI technologies like large language models and other forms of generative AI either would have developed very differently or not developed in the first place.

This raises a question: to what extent must AI technology reflect this automative impetus, or can we create other forms of AI that work very differently. I will reflect on that in this article, but I do not yet have a definitive answer.

From the Industrial Revolution to Ford’s assembly line, we have seen decades of technologies designed to automate blue collar work. That was the real idea behind factories. A shoe factory can build many more shoes much more quickly than a family of shoemakers. The technology and machinery is built to increase scale. Since the second half of the nineteenth century, we have seen a similar push in the white collar world. These jobs too became incorporated into a corporate, semi-mechanistic machine of reports and meetings that allowed corporations to churn out thinking content in a similar way to a factory. In a certain sense, computer algorithms themselves are an extreme form of this process: code are detailed instructions that a machine follows literally. Algorithms are then detailed instructions to complete various tasks or strategies efficiently, an ultimate form of mechanization.

Out this context, AI technology is just the next attempt to automate thinking. I think it seems like a qualitative jump that will increase the degree to which this is possible, more than simply a continuation in the trend, but it is still part of a longer historical trend. Nothing is really new under the sun. Many people and corporations who developed AI technology did so with the idea of automating certain kinds of thinking work in mind.

For example, many creative tasks like writing and drawing became seen as never fully automatable; sure, employers could influence the conditions in which these creative processes could happen, but on some level, a human had to sit down and actually create the art. Now that generating artistic products has become just another part of the automative process, where a program determines things randomly and the human creators may shift to a more editorial role, refining that output.

Historically, after the advent of pretty much any new technology, utopian optimists would say that this marks the end of work. No longer will humans have to work for the majority of the day; this new technology will do it for them. For example, in the 1950s, new house keeping technology like vacuums and dishwashers will allow housewives (seen as “women’s work” at that time) to complete all the housework in only a few minutes and spend the rest of their time relaxing. Similarly, utopians in the tech world have promised 4 hour work days as the latest piece of tech automates most busywork.

This never seems to happen, though. Modern home appliances did make cleaning quicker, but people increased their social expectations for how clean to expect a home to match, and suddenly, the housewives of the time spent the same amount of time cleaning as before.

Similarly, when new technology substantially automates aspects of professional work, employers end up expecting the same amount of work just with a bigger output. In our culture, we are obsessed with work, and without fixing that, new technology will not meaningfully decrease the amount of work; it will only shift the expected levels of that work and also shift what that work is.

All this relates to one of the biggest fears people have of the new AI technology: that it will steal our jobs. It has an element of truth. I don’t think it will remove all jobs forever, because our society will always invent new ways to make people work, but many specific jobs in this day and age are likely go by the wayside. Writing, for example, may shift to a type of editing, where one refines what ChatGPT does, something which may require less writers.

Within capitalism, there will always be more work to do, though: if the automative machine becomes more widespread (whether a factory, a regular computer program, or new AI technology), people will need to work to maintain that machine in complex ways. This may disenfranchise people as the skills they have cultivated no longer become useful, and these jobs could be more boring as the work becomes more and more routinized into a mechanized process.

But, we won’t see an end to work unless we see an end to our capitalist mindset that there ought to be work. To slow down, we need to remove the drive for more more more at all costs. This new AI technology or really any new technology for that matter won’t do that.

Where does all this leave us? I don’t fully know. One question for those concerned they’d loose their jobs due to AI would be, “Do you actually like your job?” Sure, you like your paycheck, but do you like your job? Some with amazing jobs they are passionate about stand to loose them, but many whose jobs are most threatened are precisely those who are already working mind-numbing drudge in the first place. They work jobs that they hate in fear that even their awful job will disappear on them.

What they really fear is an end to their livelihood. If they could have a livelihood without working their job, they’d prefer that in a heartbeat. For people in this situation, I’d suggest we rethink work in the first place. To do so, we may need to reimagine our relationship to work and profit as difficult as that conversation is.

But all this brings us back to much older, longer conversations in our society. Why work in the first place? When people talk about AI, they see new innovation coming out of nowhere, not how this stuff is the next step in a wider trend towards automation in our society. And maybe it doesn’t have to be the way it is? Maybe if we can grapple with this, we could reimagine forms of AI not built implicitly to create an ever-spinning machine churning out more and more. Such AI could be much more interesting and beneficial to humanity.

Tag: jobs

Could AI Be Different?