Given the current economic climate where large companies are laying off machine learning employees in large numbers, some may wonder if it’s worth spending four years and $80,000+ on education. not. If you compete with hundreds of candidates for a few positions on your list, how long will it take you to find a job? What salary can you expect?
These days, many machine learning positions in the US pay well below $100,000 a year, especially for beginners. Many still offer $200,000 or more, but they usually require professional experience and are usually not something you learn in school. Engineers are in higher demand than scientists, further questioning the value of PhDs. There are multiple aspects and ways to look at this.
Degree type
Some people get a degree in a field of their choice, not to get a job. If you want to climb Everest, you need a fair amount of training. Sometimes you spend a lot of money to achieve your goals, your passions, but you don’t get any money in return. The same is true for PhDs. We all know that no matter how good you are, the chances of getting a PhD and getting a decent salary and tenure in academia are extremely low. But degrees were designed for exactly that purpose, and are still offered because they are in demand. In fact, having a PhD can put you at a disadvantage in the job market, and some applicants don’t include their PhD in their resumes. There are other reasons why people do it. Recognized fame, potential credibility and reputation (if you’re writing a book), and a passion for research.
But what about a hands-on master’s degree that includes internships, real-world programming, portfolio building, and everything to impress potential employers? No, but it’s great timing for new students. First, given the shortage of jobs, it’s more productive to spend time studying than looking for hard-to-find jobs these days. But the economy for machine learning professionals will recover. I see more and more recruiters considering hiring even though they only hear about layoffs. Some of those who have been laid off do not have actual degrees, but have certificates or datacamps. There are good providers, but there are also a lot of bad ones (usually they won’t even tell you who the instructor is). These days, recruiters are more likely to ask for a real degree.
What companies usually do
Some employees were paid too much or unable to sell the value they created to their bosses and decision makers. Companies never cut salaries to adapt to new markets. Reduce hiring while at the same time bringing in new (cheaper) hires and increasing the salaries of the best employees. It happens cyclically from time to time, and each time it happens all at once like a game of musical chairs because of, or because of, the recession. Those who try to enter this market with a good education and expect a reasonable salary will eventually win.
Impact of ChatGPT
Will AI replace workers in the future, including AI developers? There is no doubt that AI can do many things, but there is also a lot of hype surrounding it. You can imagine many time-consuming and tedious tasks such as debugging and data cleaning becoming increasingly automated, but we’re still far from there. Even basic problems such as scoring fake news to real news still need to be solved. This problem can be solved without learning an algorithm (more on that in a future article). But for now, many people working on this problem, including machine learning engineers, aren’t very good at training these models with their brains, let alone algorithms. . Some companies like fake news because they generate revenue from clickbait, but they will face competition from companies that are tackling the problem.
About Hype and Future Survival
When it comes to hype, vendors try to sell expensive products like GANs and deep networks, but find buyers because of the hype. In the class I provided, I had to include that as well. Because that’s what many of our participants—machine learning professionals working in insurance, healthcare, and financial companies—would like to hear. Ultimately, buyers will realize that for relatively simple needs, a less expensive solution will probably work just as well. Markets are adjusting accordingly.
But the bigger long-term problem that will affect all jobs, not just data science, is declining, and ultimately negative, population growth. This will open up more and more positions in healthcare and related fields for older people. Addressing the climate issue is unlikely to lose momentum anytime soon.
About the author
Vincent Granville is a pioneering data scientist and machine learning expert, founder of MLTechniques.com and co-founder of Data Science Central (acquired by TechTarget in 2020), VC-funded Former executive, author and patent owner. Vincent’s past corporate experience includes Visa, Wells Fargo, eBay, NBC, Microsoft, CNET and InfoSpace. Vincent is also a former Postdoctoral Fellow at the University of Cambridge and the National Institute of Statistical Sciences (NISS).
Vincent published number theory journal, Journal of the Royal Statistical Society (Series B), and IEEE Transactions on Pattern Analysis and Machine Intelligence. He is also the author of Intuitive Machine Learning and Explainable AI. You can see from here. He lives in Washington State and enjoys studying stochastic processes, dynamical systems, experimental mathematics, and probabilistic number theory.
