Stories from the last downturn
Life keeps happening
For the first time in a while (for tech at least), fear about an economic downturn is in the air. Along with the big stock market drop since January, with most things down over 20% and some down significantly more than that, there’s been increasing news of employment woes. For example, Coinbase pulled a pretty jerk move of rescinding accepted offers, hiring slowdowns/freezes at places like Meta and Microsoft, outright layoff announcements at places like Tesla, Robinhood, and Paypal.
Since the last really bad tech-related downturn coincided with the big 2008 financial crash and the resulting fallout from there, it’s dawned on my aging brain that many people working in tech now have never been through a period where the VC money stops flowing like cheap beer at a college party.
Now’s probably a good time for some stories about tech life around the previous downturn.
I’ll preface this by noting that survival bias is a powerful drug. Luck and circumstance probably had more to do with where I’ve currently wound up than any outstanding talent on my part.
So let’s start with a fact:
I’ve been laid off at 3 out of my 6 full-time positions
The 6 total positions include my current one. There’s a lot to unpack from that statement.
Yes, I’ve been at only 6 places in ~15 years (longest tenure being ~6 yrs). I have a tendency to stay put in organizations for longer than is probably good for me
When the chips are down, data positions are surprisingly expendable
My longest stint of unemployment was (roughly) 4ish months
There’s plenty of life after layoffs and other forms of losing your job, downturn or not
By now, I’m fairly confident that no matter where I go, I’m always one executive meeting (that I’m not involved in) away from getting the axe. Current mega-corp employer not withstanding, having an affinity for early/mid-stage startups tends to increase the chances of getting axed with little warning.
It’s a life I’ve become acclimated to and is probably not for everyone. I will admit that my appetite for employment risk is declining rapidly as my family responsibilities grow. My way of coping is I do my best to maintain a very fat emergency fund (> 6mo) that’s strictly for “in case I can randomly axed”. I’ve luckily never really had to tap into it much.
Data positions are surprisingly expendable
This probably comes as a surprise now when data is such a ridiculously hot field that seems to bring so much value — but I’ve been the sole data analyst-like person at multiple places, with good working relationships (and projects) with practically everyone in the company, and when the finances of the companies get bad, I still got the axe. My only consolation is that I’m usually axed alongside other “competitive” positions like software engineers. If being a single point of failure for a number of analytic processes wasn’t enough to qualify for safety, there’s not much out there.
This pattern highlights an important aspect about data work. The data function that’s my primary expertise — the analyst/researcher that amplifies team effectiveness by providing insights that help teams understand their situation and make smarter decisions — is almost always a cost center. My work only really generates revenue indirectly — the impact is hard to measure and complex.
When a company is under severe financial stress, where budgets must be cut to ensure the continued operation of the company, they will inevitably choose the path of making poorer decisions using pure intuition and guessing over bleeding money by paying my salary.
This is a perfectly rational decision, and I don’t particularly fault the executives for making such calls in the multiple times it’s happened to me. It’s not clear that paying me 6-figures in salary and benefits for another 6 months would actually generate 2x, 5x, 10x the revenue back. Especially if keeping me around means they’d have to cut another engineer that can build sellable new features that does generate revenue. Plus, the company had flown blindly without a data analyst for years before hiring me, they can survive in the short term without an analyst again and re-hire one later when the danger is past.
So in the grand scheme of things, data positions are valuable to keep around, but a very long way from being “safe”. Our relatively high salaries can sometimes even make us juicy targets when budget cutting times come around.
Also, don’t think your job is safe simply because you built out the magical recommendation system that generates millions of revenue. How long can it run as-is without you? And how confident are you that the organization won’t give it a try?
Honestly, nothing is truly safe unless you’re the founder with a controlling stake in the company and it’s best to just be aware of the fact.
Longest unemployment period was about 4 months
That four months was quite early on in my career, right after the 2008 housing crash that decimated the NYC financial sector. It was the worst stretch and I also was the least prepared since I had only been working about a year then. I’ll get more on this particular episode further down.
The other two instances of layoffs, I was back in another job in about a month on the short end, and about 2-3 months for the other. The short downtime was likely thanks to the market for data science being unbelievably hot during 2010-2018, which meant there were plenty of data positions out there for a product-focused generalist. But in addition to the market, having the chance to establish a bit of a professional reputation in the years before helped greatly.
I stand fairly high in the introverted and socially awkward scale of things — especially with unfamiliar people. I have been to data conferences and spoken to perhaps 3 individuals the entire time. Since I’m hopeless at talking to people in person in ambiguous social settings, my primary method of building a professional network is just working with plenty of folk, making friends, and then those people leave for other companies over time. My other strategy involves talking to people on Twitter and the very occasional Meetup event. This newsletter is also effectively a network extension, though that’s not what I write it for.
Anyways, that small network of tech industry folk that I had built up over 5-8 years of working and tweeting translated into the occasional lead for work in my times of need. Between that and blasting a flood of resumes out every few days, things worked out.
That’s all well and good for older folk, but what if you're early in your career and don't have any sort of network yet?
Find ways to make friends with other data folk long before you need any favors. In the immediate timeframe you’ll have people to share ideas and banter with. And in times of need they’ll help out by lending you bits of their network. That’s assuming that they feel positively about you. People in general like to be helpful to others. Just remember to return the favor when you see others in need of help.
So about that 2008 crash - things eventually work out
Back to the big crash of 2008. I had been working in an interior design consultancy, effectively as a data analyst. I’d be pulling insights out of space survey data, and writing demonic Python+VBA scripts to automate the generation of slide decks and analysis. Then the crash happened a couple of blocks away at the stock exchange and all the banks that were our clients canceled their consulting contracts. Since I was entirely back-office and not generating precious revenue, I was unsurprisingly let go into the smoking crater that was the finance-heavy NYC job market.
The only thing to do was to apply for unemployment and just apply to any work that seemed like I could do. It wasn’t exactly a time to be super picky so I scanned just about anything that mentioned working with data and analysis. If anything, being at the very start of my career meant that I had one advantage — I was relatively cheap.
Either way, it’s good to remember about economic downturns is that positions do continue to open up due to natural worker attrition and other factors. It’s just much more competitive and you’ll likely have to balance compromises when searching for positions. It becomes a numbers game where eventually, if you keep grinding away at it, something will work out. It’s not fun and you have to be very careful not to burn yourself out from stress.
Most important to remember is that our value proposition as data folk is broadly applicable — you [company] have data, I can help make you and everyone else nearby, smarter through the use of that data. Therefore, hire me. Over the years, I’ve personally dove into interior design, ad-tech, a social network, e-commerce, and enterprise SaaS using that same value prop. Being a generalist is my insurance policy.
Ultimately, I wound up joining a tiny ad-tech firm of maybe 15 people also based in the financial district. That place had a lot issues with it, including some of the worst bosses I’ve ever encountered. But also, this was where I learned SQL on my first day and was then drilled into writing performant MySQL queries on pain of a yelling CEO.
Despite the bad things, that job wound up becoming a turning point in my career. I got really good at SQL, and got into the tech industry where my nerdy hobbies like learning FreeBSD on my own was surprisingly valuable. I later jumped ship to Meetup thanks to the skills I learned, and I made tons of friends and grew into my product development/quantitative UX research niche there.
Things eventually wind up working out. You have to remember that almost everyone over 35 was very likely working during the last downturn and most of us are still here and successful.
It’s definitely not all champagne and roses when the bubble inevitably bursts, but it’s not all darkness either. Just remember how greedy algorithms do surprisingly well in many optimization problems. Apply that by going through tough parts in life and taking the best choice immediately in front of you while making course corrections later.
Take some deep breaths, take a couple of hours away from grinding out applications. It tends to work out.
Standing offer: If you created something and would like me to review or share it w/ the data community — my mailbox and Twitter DMs are open.
About this newsletter
I’m Randy Au, Quantitative UX researcher, former data analyst, and general-purpose data and tech nerd. Counting Stuff is a weekly newsletter about the less-than-sexy aspects of data science, UX research and tech. With excursions into other fun topics.
All photos/drawings used are taken/created by Randy unless otherwise noted.
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