Taming a Tough Job Market, Part Two
In last week's post , I discussed the three steps to making the unknown known: compare, itemize and test the correlations, and develop experiments to fill in the gaps. I also discussed the basics that form the foundation of your next job search. Now we are ready to go into the details. Using our comparison of the job market to the housing market, what do we know and what can we test? What actions can we take to tame a tough job market?
Availability
What we know: There are fewer job openings for external hires. In part, this is due to the redirection of investment from people to infrastructure. But it’s also due to valuation. Some companies may not want to increase their total headcount because that affects market capitalization at a time when there is intense focus on profits. These companies may prefer internal transfers, i.e., shifting their headcount around to keep the total number of employees level year-on-year. This equates to people holding onto their homes, not putting them on the market, because new mortgage interest rates are unattractive. So we have an availability problem.
What we can test: Just like some homes are selling, there are postings getting filled by external candidates. How do you find them?
- There is still funding and investments, albeit not at the record levels we had when interest rates were near zero. These new companies will need new hires. To find the new companies, consider subscribing to Investopedia, The Information, Bloomberg Tech, and even the VCs’ newsletters. When start-ups get money, they may have jobs.
- There are still growth markets. In areas of growth, companies will have to add headcount to support it. But they will be highly selective and yet move very quickly. Just like the buyer being ready to pounce, you want to make yourself ready. But ready for what? One experiment is to research your LinkedIn feed to see who is changing jobs, and from what to what. The “to” may be a growth area, and an option for you if you can match yourself to that.
- Mining data. If you can afford it, the higher-tier LinkedIn paid memberships make more data visible, including who is looking at your profile and whose profile besides yours they are looking at. What industries are these lookers coming from? What parallels can you draw from your profile and the others? This could reveal what areas may have unpublished or emerging openings.
AI (The Job Bots)
What we know: Perhaps you’re familiar with services that claim to use AI to automatically adjust your resume and submit your application to matching positions. It seems clever, but the unintended outcome is that for any one job opening, there end up being up to tens of thousands of applicants – many of whom not qualified. I feel for the recruiters who have to sift through all that, although the truth is the initial triage may be done by bots as well.
What we can test: There is a wealth of information we can glean from the output of these types of services. It would be useful to know what changes the AI job services are making to your resume. A friend of mine recently signed up for one of these services for that purpose. She discovered that the service omitted results and impact statements (not intentionally, for sure). If you find yourself stuck on what variants you need to prepare for your resume, using a service like this might generate ideas. At the very least, it could show you examples of what not to do! And you will be more informed about what types of applications those “thousands” might submit and could add explicit differentiation to yours.
Another source of data would be the categories of jobs the “AI” thinks are a good fit. Not only are title and level helpful, it would also be informative to dig a little deeper. It could be that the bots aren’t very good, like if you are a Principal Engineer and it matches you with Level 2, or to a sous chef. But if it makes a match with an unexpected yet adjacent position, you might want to ponder why. There could be something about how you are conveying your experience that is misstating your capabilities. Or there could also be an related role that would be an excellent fit, but you haven’t considered it before.
New Skills
What we know: Technology firms are investing heavily in AI, whether that’s infrastructure, engineers and scientists, or AI/ML services. It may seem like every job opening we see implies the need for someone with AI skills. What we know is that the field/specialization of AI is still new enough that, unless you are a researcher, you probably do not have sufficient direct experience. What you can show off, however, is how quickly you learn and how innovative you are in applying your learnings to your job. This is a form of indirect experience that may be attractive to the kinds of people you want to work for.
What we can test: There are several options available to you to develop some AI/ML experience. The best is to find a small project to work on. Kaggle contests are great, although they can be time- and resource-consuming for an individual. You don’t need to win one, but if you submit a solution that is ranked, it is something you can show off just like your GitHub repo. Kaggle is also a great way to build community, and maybe get invited to join a team to work on future projects for prize money.
If you are already savvy in data science, it would be clever to set up a notebook to analyze and apply natural language processing to the postings returned by your job search agents (see “covering the basics” in the previous post ). You could even use this as an example of your indirect experience. Some questions you could answer:
- What is the range of experience for a given job level, and does that match yours?
- What are the most common terms used when itemizing skills? What percentage of these do you have?
- How many of the common terms show up in the postings that the AI job bots identified for you, assuming you ran that experiment?
- Is there a strong correlation between the industry and the common terms?
You may have noticed I use AI, ML, and DS (data science) in this section almost interchangeably. My view (others may differ) is that AI is an application of ML, and ML is an element of DS. And data science is everywhere, even if not called out explicitly. Where can you emphasize that you have applied DS? It relies heavily on principles of statistics; what else relies on statistics? Test and QA come to mind. Data science is a science because it has scientific methods at its foundation, i.e., the creation of hypotheses and tests for the likelihood of those hypotheses being true. Where have you already done that, that you could call out as related experience?
Referrals
A referral is a definite advantage for posted jobs. Even more, a referral can get you considered for roles that are not yet posted. A study cited in Herminia Ibarra’s Working Identity suggested that over 90% of undiscovered opportunities come from connections – specifically 2nd-degree connections. Why 2nd-degree? Because the strength of the referral gets you considered, but the fact that it isn’t someone who works with you in your current role means that you aren’t pigeonholed into that company or that position. Second-degree connections are more likely to think outside the box because there isn’t a box to begin with!
Building out your connections to increase the possibility of a referral is a recommended action. But don’t ignore the rest of your network. Networking is table stakes for the job hunt, so stay in touch with your connections. Don’t be a pest, but avoid assuming that if you don’t hear back immediately, they are ignoring you. Those who are not looking for the next job are operating on a different timeline than you. What feels like a month to you might feel like yesterday to them.
This concludes my exceptionally lengthy, two-part post on finding a job. I hope that some of these explanations, actions, and experiments resonate, that they give you a sense of control over the parameters to make the outcome what you want it to be. With this data, the “tough” job market becomes a known one. And if you know it, if you understand it, you can change it. Good luck, and let me know how I can help.
