2019 Data Science Jobs Market Outlook
Data science jobs are still a hot market in 2019. Wondering exactly what the data science jobs market looks like? Are you looking to find an entry level data science job? What's the outlook for women in data science? Find out what our friends the data science recruitment experts at Burtch Works think the outlook is for future data scientists and analytics professionals.
A Conversation with Burtch Works About the Data Science Jobs Market
At QuantHub we’re always trying to stay on top of hiring trends and data scientist skill demands in the data science job market. We recently had the pleasure of chatting with Linda Burtch, founder and Managing Director of Burtch Works, a data science and analytics recruitment firm, and her colleague Katie Ferguson, who handles the firm’s early career and mid-level analytics jobs practice.
Burtch Works is a Chicago-based company that has been specializing in quantitative and analytics talent recruiting for over a decade. Burtch Works’ annual Data Science and Predictive Analytics Salary Reports as well as its “flash surveys” and other analytics industry studies are highly anticipated and followed by thousands of data science experts and companies.
On the heels of the firm’s most recent 2019 Data Science and Predictive Analytics Salary Report, we discussed with Linda and Katie what the outlook is for data science and analytics professionals of all levels. We covered topics such as what impact new data science tools will have on the job market, what China is up to, million-dollar questions such as “are bootcamps worth it?” and more. Read on to learn more about what these two data science industry experts are seeing real time in today’s data science jobs market.
A Word on Data Science Job Descriptions
Before you read on, it’s important to note that in its 2019 salary survey Burtch Works divides data analytics professionals into 2 segments:
- Predictive Analytics Professionals (PAPs) – These are analytics professionals who are distinguished from other quants by the amount of data with which they work, which is well beyond what can be managed in Excel. They work primarily with structured data sets.
- Data Scientists – These professionals are a subset of PAPs. These professionals tend to have more of a computer science and coding background, and work more with unstructured “messy” data. For this reason they typically command higher salaries than other PAPs.
“Million Dollar” Questions on Data Science Job Requirements
Is Python “enough”?
Katie said that more and more junior and mid-level professionals are going direct to using Python. Some reasons for this are that academic programs are teaching Python and that Python allows professionals to write production-ready code.
Burtch Works has been tracking the use of Python and R among data science professionals for over 5 years in its annual flash survey, which currently boasts over 1,000 participants. Katie says that,
"Python has already surpassed R as the language of choice for early career analytics professionals."
Do you need a master’s degree or does a data science bootcamp suffice?
(Aspiring data scientists take note!)
According to Burtch Works’ 2019 Salary Survey,
86% of PAPs and a whopping 94% of Data Scientists hold an advanced (master’s or Ph.D.) degree.
(That is tweetable info, yes!)
Linda and Katie estimate that in their personal experience the vast majority of entry-level hires for both data science and predictive analytics jobs have advanced degrees. This is still the “traditional ticket to entry” they have observed.
And as for those Data Science bootcamps?
When asked if someone with a nontraditional education can earn as high a salary in a data science job as someone with a Master’s Degree in Data Science, the Burtch Works recruiters admit that since these programs are newer they don’t track data on salaries specifically related to bootcamps yet.
That said, they generally see candidates with a master’s degree commanding higher salaries than those with a bachelor’s degree. The data from the 2019 Salary Survey supports Linda and Katie’s observation:
"Survey results indicate that at most job levels, except at the highest managerial levels, professionals with advanced degrees typically earn a higher salary than those without."
The issue with bootcamps, Linda points out, is that they tend to focus on coding. But data science is not just coding, she emphasized,
“Math and statistics take years of study to master.”
In Linda’s experience, a candidate can’t learn all that he/she needs to know about these subjects and be able to apply it in a data science setting by taking an 8-12-week bootcamp. “Sure, you might learn a few tricks, but you can’t develop the quantitative rigor typically required in a data science role,” she adds.
That said, Linda and Katie do see a role for bootcamps for people with a related background, such as physics, and who want to pivot into the data science field. They see this as a real possibility after doing a bootcamp.
However, can someone with a political science degree move into data science solely by doing a bootcamp? “Depends on the role you have in mind,” Linda advises.
So, there you have it - words of wisdom to heed from experienced data science recruiters!
What mistakes do junior level data science candidates make when applying for data science jobs?
Linda and Katie say that
"Candidates who lack internships or real world data science experience will be at a real disadvantage in interviews for data science positions."
They recommend that candidates should really find a way to get hands-on experience with messy data before hunting for a data science job. They remind us that data science is about what you can do rather than what you know.
Salary negotiation for data science positions has gotten more complicated recently due to salary history laws in many states. But, Linda and Katie find that most professionals are still transparent about their salary, so be wary of over-negotiating. Salary is only one factor to consider when evaluating a potential job offer.
What advice would Burtch Works give to aspiring Data Scientists?
Linda has three pieces of advice for people who want to get into the field:
1) Get a master’s degree in a quantitative field
2) Do an internship and get practical experience
3) Get practical experience working in data science with messy data (i.e. Kaggle or other competitions)
Companies and Hiring
Will new predictive analytics and visualization tools make PAP roles less lucrative?
Automation doomsdayers beware and analytics graduates be reassured. Linda and Katie’s short answer to this question is “No, there’s just not enough talent.”
They agree that while new analytics tools are becoming more sophisticated, they are still far from perfect. They add that you still need people with the talent who know how and when to use these tools. Plus, these tools are continually evolving and someone needs to learn how to use them wisely!
Should companies be doing more to retain analytical talent?
Linda’s short answer is “absolutely”. According to Linda and Katie, top analytics talent are now conditioned to ignore outreach on LinkedIn from recruiters. That said, they remind us that Data Scientists and PAPs tend to prioritize opportunities to learn and develop on the job. For this kind of talent group, Linda adds,
“It’s not all about the money.”
The atmosphere of the team, the company industry, work-life balance offered, location and developmental opportunities are all factors that contribute to retention of data scientists.
Nevertheless, Linda notes that Silicon Valley has actually coined the term “tour of duty” to describe many data scientists’ attitude towards career planning. Many data scientists will seek out new experiences after only a few years in each role, but this practice is accounted for in the Silicon Valley business model and even encouraged among tech companies. Linda thinks that this level of turnover among data science talent is likely to continue.
She also believes that this turnover, which is driven by a desire to grow and seek out new challenges, is, in fact, healthy overall for the analytics industry. She advises that companies should thus consider building this dynamic into their business models, at least in the short term until the discipline matures.
Are HR Managers becoming savvier with respect to understanding data science job roles?
According to Linda and Katie’s experience, although it’s certainly not the case for all companies, there is often a general misunderstanding about the difference between PAP and Data Scientist roles. Because the term “Data Scientist” is so commonly used now (Katie adds that even her father knows what Data Science is now!) and because there is not necessarily a consistent definition across different companies, it has become an overused term for analytics positions.
This can sometimes contribute to misunderstandings on the hiring side. Luckily, Linda sees that hiring managers are savvy enough to know their local universities and talent pool profiles from which they can recruit for data science positions.
The Demographics of Data Scientists and PAPs
Where do data science and analytics professionals want to live?
Young analytics talent is still drawn to the urban areas, despite the high cost of living, says Linda. As an example, San Francisco has been a tech talent magnet for the past eight years or so. However, as this talent matures, it becomes difficult for them to build a life there and so they are starting to seek opportunities elsewhere.
The good news is that due to the broadening application of analytics in other industries and regions, there are many more job opportunities in data science and analytics outside of the bay area now.
On the lack of women in data science and analytics roles
The 2019 Salary report indicated that just 17% of Data Scientists are women vs. 26% of Predictive Analytics Professionals. (This coincides with other industry reports on gender that we’ve seen) While at first these numbers may seem somewhat low, even as she acknowledges that data science has traditionally been a bit of a “boys’ club” Linda is quick to point out that
"37% of PAP junior roles (up from 32% in 2018) and 32% of entry level data science roles surveyed were filled by women."
She predicts that we are likely to see healthy growth in the number of women entering the data science analytics field.
As for the lack of women at higher levels of management, as well as in senior Data Scientist jobs, Linda and Katie attribute at least part of this to the personal responsibilities that women face. It’s most definitely not for a lack of coding skills. Linda adds,
“Women are great coders. They are naturally organized thinkers and are into the details. These traits go well with coding.”
It’s mostly an issue of time commitment and finding work-life balance, she explained.
Differences between foreign analytics professionals and US citizens
At QuantHub, we see a lot, in some cases a majority, of foreign-born candidates taking our data science assessments and participating in our data science skills challenges. The US tech industry especially has long relied on foreign talent to fill the domestic skills gaps in data science.
So, we asked Burtch Works what dynamics they were seeing with respect to salaries, skills and jobs when comparing foreign vs. US citizens. Their 2019 Salary Survey indicated that roughly 1/3 of their sample of PAPs and Data Scientists at the early career level are foreign born. In general,
Burtch Works sees foreign born nationals earning slightly more than their US counterparts.
Why is this? Well, the biggest shortage of data talent is at the mid-level individual contributor and the junior manager levels, where 6-12 years of experience is needed. Foreign nationals who fill these data science jobs usually hold advanced degrees. They are also more likely to be willing to relocate to any location in the US vs. US citizens who may have a preferred location to live. Because of this, these foreign candidates are often casting a wider net and will sometimes get multiple offers. These factors tend to push their salaries up.
Where are these foreign nationals coming from? Mostly from India and China, Katie says, adding that oftentimes, Indian citizens will have prior experience in data analytics before coming to the US.
Anecdotally, Linda also noted that more Chinese companies are coming to the US to recruit data science talent to return to China to work. Sometimes this talent will take a job in China, but leave their family in the USA to live, and share apartments in China while traveling back and forth every few weeks.
What's the Outlook for Data Scientists and PAPs?
All told, Linda, Katie and the team at Burtch Works, a team with their finger on the pulse, see no signs of a slowdown of hiring in the data science and analytics field. The results from their early 2019 survey of companies depicted in the figure below confirm this.
Burtch Works cites opportunities created by the broadening of data analytics into new industries and new regions, the development of new use cases, and the continuous evolution of associated technologies as reasons that the future looks bright for analytics professionals at all levels.
There’s More in the Data Science Salary Report!
There are a host of additional topics covered in Burtch Works’ 2019 Salary Survey including details on salaries for 6 different job levels for both PAPs and Data Scientists, regional comparisons, skills and industry-specific information and much more. We highly recommend downloading it, especially if you are in the data science job market or looking to grow your data science team.
If you prefer to listen to a summary of the 2019 Salary Survey results, you can watch the webinar here.
Finally, we’d like to extend a special thanks to Linda and Katie and their team at Burtch Works for an insightful discussion. We look forward to seeing what insights and trends their future studies reveal about data science and analytics jobs!