Hiring across data science is returning to normal patterns, and the great rebuilding process has begun with FAANG and AI leading the pack.
From July to August of this year, almost all data science positions saw month-over-month increases. Data scientist positions saw the highest gains, with a 25% increase in job openings.
To investigate possible seasonality variables, we compared this recent uptick to the same July-August period in 2022, which experienced a similar bump. Industry veterans note that companies typically have their second big hiring push during this late summer-early fall period as they close out headcount and budgets before the end of the year.
Taking all of this into account, the 2023 numbers offer reassuring news as we see the overall job market stabilizing through the summer and returning to normal hiring patterns. We expect the rebuilding to continue from here.
However, looking at hiring year over year shows just how far the industry still needs to go.
YoY, openings are still much lower compared to 2022. Data science and data engineering roles haven’t even come close to rebounding from the late 2022-early 2023 mass layoffs in Silicon Valley.
The one standout is machine learning engineering, where positions managed to increase by 16% YoY. However, this could just be a variance given the relative rarity of this position compared to other job openings– in hard numbers, the position only increased from 618 to 722 openings.
Overall, a return to pre-2022 layoffs isn’t immediately in the cards, but the situation is improving incrementally. However, the Class of 2024 should keep an eye on the rearview mirror. As noted in our State of the University Report, we’ve seen huge growth in Business Analytics and Data Science programs. As the funnel continues to get more crowded at the top, job competition will only grow, especially if the market doesn’t bounce back aggressively.
In January of this year, these companies made up only 0.5% of the overall tech job market, down from a 6.5% high in the spring of 2022. Mid-way through September 2023, FAANG jobs have managed to bounce back to 2.7%. Microsoft, Amazon, and Meta are all re-hiring for data science and software engineering roles, pulling FAANG up as a result.
While these numbers are promising, they’re still a far cry from the high 6.5% market share in early 2022. Adding and dropping companies from the larger FAANG list yields slightly different results, but candidates should keep an eye on the broader data science-powered industry for the time being.
Don’t count anyone out– tech companies have begun to adapt to this slow growth market and AI boom, even if they can no longer rely on growth from the zero interest rate phenomenon that we saw for two years post-Covid.
Anecdotally, we’ve spoken with multiple Meta recruiters who’ve confirmed that hiring is back on for data science roles, especially so for AI research. Many recruiters at Big Tech companies are trying to win the race for AI talent, which is why that role is pulling away from the rest of the data science industry.
Recent anecdotes about the rise of AI engineering and AI research roles have solid evidence behind them that we’re tracking closely. While research scientists are always in demand, we’re noticing some new AI engineering jobs that don’t require a PhD but rather require an in-depth knowledge of LLMs and prompt engineering.
Looking specifically at job postings that mention AI and research/engineering together in the title, we see a new peak in total unique job count.
Industry demand for these positions has contributed to extremely high pay. For example, Anthropic has a job opening for a “Prompt Engineer and Librarian” with a 300,000 USD annual salary. For engineers who can work on AI and LLM models, there’s a median salary of 900,000 USD.
This is a field that has existed for less than two years, so most employers aren’t expecting 10+ years of experience in AI and LLMs (hopefully).
The AI engineer role will be interesting to track as the industry begins to figure out the precise parameters of this position, which combines product management, data science, and software engineering. They’ll also be deciding exactly how this role differs from software engineers who primarily work on deep learning, LLM systems, and research.
In the next 6-12 months, we’ll see if the rise of AI engineering is an actual trend or just a temporary fad. So far, it’s proven to have strong growth and short-term resiliency. Anecdotally, we’ve also seen a lot of high-caliber teams raise millions of dollars in the AI space with just a starting idea and without any infrastructure in place.
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