Summary: The annual Burtch Works salary survey with data through April shows that opportunities and salaries are still excellent for both new and experienced data scientists. They also offer some anecdotal observations about the impact of the first few months of COVID on our work and opportunities.
If you’re planning on becoming a data scientist, particularly if you’re just graduated or about to graduate you’re probably wondering how much of a dent, if any, COVID has made in DS opportunities. The news is generally good.
To start with, let’s stipulate that the best credential is still an MS in Data Science. If you decided to go all the way to Ph.D. good for you. You’re knowledge as a Ph.D. is likely very specialized and targeted at one of the niches in DS. There are plenty of Big Tech companies anxious to make your acquaintance. About half of Ph.D. data scientists and others with those specialty skills are working in Big Tech.
For most of us however, the MS credential gives just as good a career outcome with very little penalty in salary, outside of those specialized Big Tech companies. The data show that where many of earlier entrants started with business, engineering, other hard science, or math, today’s new crop knew they wanted to be data scientists and focused early on computer science and predictive analytics.
While the most value and the most jobs across industry (aside from Big Tech) is still created by traditional machine learning skills, the demand for specialties around NLP and text, and computer vision and image classification with DNNs is growing greater.
The days of the DS generalist or unicorn are already behind us. In most careers, including DS you increase in value as you learn more about a specific industry. Increasingly you may also find yourself specializing in ML, NLP, or image classification within your chosen industry. So if your desire and skill set is to work with DNNs there are plenty of opportunities outside of Big Tech to specialize in image and text.
Fortunately every year about this time we get a nice quantitative look at our career field thanks to the good folks at Burtch Works Executive Recruiting. For many years they have been giving us the benefit of their extensive knowledge of our job roles, skills, and salary levels thanks to their extensive relationship with several thousand data science job seekers. The data we’ll share here comes from their most recent 2020 study.
This year’s salary and demographic data survey was wrapped up in April so some of the COVID impacts aren’t reflected but Burtch Works has offered some follow up observations. It’s fascinating stuff and looking both directly at their results and reading a little between the lines we can get some really good insights.
At Least Through April Salaries Were Stable or Rising Slightly
Back in the early days of this survey you could see salaries rising rapidly at all levels. Since at least last year however they’ve become much more stable.
Burtch differentiates between individual contributors and DS managers. Among individual contributors, for the last year, there were average 2% or 3% rewards for more experience. Among DS Managers there were some increases at the junior end, but with senior managers averaging $250K, the top end was flat.
We observed last year that even with the greatly increased graduation of new data scientists, year over year salary increases had largely leveled out. Our conclusion remains that the shortage of data scientists that existed two or three years ago has largely gone away. Even though companies across the board are expanding their data science staffs, supply and demand are now roughly in equilibrium. The salaries however remain excellent.
Predictive Analytics versus Deep Learning
Burtch Works continues to make a distinction between ‘Predictive Analytic Professionals’ and ‘Data Scientists’. Their position is that the companies who retain them to find these folks want two distinctly different skill sets. To Burtch Works’ way of thinking it comes down almost entirely to what we know as deep learning and the ability to write code.
Based on this distinction, Burtch Works finds that their ‘data scientists’ are the ones working with unstructured data and deep learning with far more Ph.D.s in their data science category. Overall their “data scientist” category does have a salary advantage, but that could also be explained by other factors such as almost 50% of them working in Big Tech.
I continue to have a problem with this simplistic dichotomy. I expect you and I both know plenty of ‘Predictive Analytic Professionals’ who regularly work in unstructured data, write in Python, and are comfortable around text analytics, image processing, and computer vision.
Opportunities for Women are Increasing Rapidly
In this era when we’re hyper concerned about equal opportunities it’s great to note that in just one year, entry level hiring for women jumped from 23% to 37%.
What About COVID
Some of my own observations combined with those of Burtch Works about what has likely occurred in the last few months.
Data science remains the defining focus in the so-called digital journey that all companies are pursuing. Hiring was strong up to March and most respondents to the survey don’t report layoffs or salary reductions.
However, if you’re in travel, leisure, and maybe retail your story is different. Travel and leisure together make up 10% of GDP so we’ll have to see how long that segment takes to recover. Data scientists however may be the key to retaining or growing what business exists in these depleted segments.
If you’re not in one of those impacted segments, my personal observation is that many large companies are wisely using these troubled waters to double down on strategies and implementation that rely on data science. That can mean more hiring opportunities and faster growth outside of travel, leisure, and retail.
Work from home (WFH) has had a particular impact on the Big Tech centers in San Francisco, Seattle, New York and some other major metros. Now that we know we can successfully WFH many of us won’t be going back.
That means a migration to less expensive and more pleasant places to live and it may ultimately mean that the 50% of jobs held in Big Tech will experience downward salary pressure as the high cost of living is less relevant to hiring salaries. If you had to trade a few dollars salary for a much more pleasant and less expensive environment that probably wouldn’t be so bad.
Burtch also reports that the time to hire, now mostly remote, and new hires starting as WFH (no relocation lag time) is resulting in faster hiring and faster on-boarding. All the easier to find that new opportunity and show your value sooner.
About the author: Bill is Contributing Editor for Data Science Central. Bill is also President & Chief Data Scientist at Data-Magnum and has practiced as a data scientist since 2001. His articles have been read more than 2.1 million times.