The data science job market is growing faster than most others, as companies of every shape and size look for professionals to solve their data problems.
Whether it’s modeling and analyzing data sets or preparing data for machine learning (ML) projects, companies need more data science talent and are sharpening their recruitment and retention strategies in that area.
Read on to learn more about the trends that experts are seeing, both from the recruiter’s and the candidate’s perspectives, in the data science job market:
5 Top Data Science Job Market and Career Trends
- Upskilling and continuing education as No. 1 priority
- Favoring data science specialists over generalists
- Candidates looking for a strong company mission
- Companies ill-prepared for data science hiring and projects
- Rounding out data science teams for digital transformation
Also read: Top 50 Companies Hiring for Data Science Roles
1. Upskilling and continuing education as No. 1 priority
Many data science candidates are growing more interested in the educational opportunities a potential employer can offer them.
Scott Hoch, head of data at Revenue.io, an artificial intelligence (AI)-powered RevOps platform provider, believes that many less experienced data professionals are hoping to find a company that will provide supportive mentorship and learning opportunities as they look to expand their skills.
“When I talk to people in this community, either coming out of boot camps or who are making the switch into data science, they’re always looking to learn,” Hoch said.
“For these people earlier in their career, they’re looking for mentorship, looking to grow, and they’re looking to understand how data science fits into a real-world project.”
David Sweenor, senior director of product marketing at Alteryx, a data science and analytics company, said that candidates want to keep abreast of the latest trends in the market and work for a company that allows them to apply that learning to their projects.
“Data science candidates are looking to use some of the latest machine learning innovations and techniques to solve real-world business problems,” Sweenor said.
“They want to become a trusted adviser to business leaders and want interesting problems to solve. They don’t want to become order takers for BI teams.”
Many companies are listening to candidates’ desires for additional education and are finding ways to incorporate skill-building through partnerships in the education sector and learning resources.
Sunil Senan, VP of DNA at Infosys, a global IT services and digital transformation company, explained how companies are finding new ways to invest in their existing talent through a mixture of certifications and courses for non-technical employees.
“A key trend in the data science job market includes enterprises investing early in the talent pipeline through partnerships with educational institutions, training programs, and upskilling from within,” Senan said.
“Businesses are partnering with institutions to ensure the correct educational opportunities are being implemented at universities, along with creating strong training programs for digital careers.
“For example, Salesforce launched a free online learning platform, Trailhead, where aspiring tech professionals can earn resume-building certifications and tech skills. We recently announced a new program with Trailhead to offer 500 job seekers the opportunity to complete an online diploma course certified by Salesforce, following an aptitude test matching them with entry-level tech roles at Infosys.
“Training platforms such as these are building strong tech candidates, giving non-tech professionals the opportunity to gain more expertise within fields like data science, which in turn helps enterprises upskill non-tech employees from within.”
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2. Favoring data science specialists over generalists
When many data scientists first come out of a degree or certification program, they know how to code and generally pursue data science projects, but they might not always bring specialized skill sets and experiences to the table.
Companies are now searching for data science specialists who can quickly apply their existing skills to a specific problem the company faces.
Kevin Pursel, VP of recruiting at Doma, a real estate tech solutions company, shared why his company and others like it are looking for data science specialists over generalists.
“As opposed to hiring a statistician that can write a few lines of code, now we’re looking for far more niche skill sets, such as deep learning engineers, NLP engineers, computer vision engineers, risk data scientists, machine learning engineers, data engineers, and even machine learning operations engineers,” Pursel said.
“It’s getting more and more difficult to grow as a data science generalist. Companies want to hire specialized people that can push the capabilities of their products and organizations.”
Although many others agree that specialists have more earning and growth potential than generalists in the current data science job market, it’s important to note that generalists might have more of an opportunity at smaller companies or those that are just starting to focus on data science.
Hoch from Revenue.io explained how large and small companies have different expectations for their data science talent:
“There’s a divide between what large companies are looking for in data scientists and what smaller companies and startups are looking for,” Hoch said.
“That divide is growing. At larger companies, they already have a lot of the infrastructure in place for managing their data and cleaning it up. They’re looking for data scientists and researchers to come in and just go very deep on data science problems.
“Whereas startups and smaller companies might not have all of that data science and data infrastructure in place. They’re looking for jacks-of-all-trades who can start getting insights out into production and work on more of the stack. So a lot of people are coming into data science, and the divide and needs between larger and smaller companies seem to be growing.”
Also read: Key Machine Learning (ML) Trends
3. Candidates looking for a strong company mission
Data science careers are hardly limited to tech companies or the “Fortune 500”; companies of different sizes, locations, and industrial backgrounds are hiring for data talent across the board.
Data science candidates recognize they are in high demand right now, and as a result, many are looking for companies with missions that align with their personal interests and values.
Stuart Davie, VP of data science at Peak, a decision intelligence company, said that what a company stands for can be a huge draw for data science candidates.
“Data scientists in general are relatively deep thinkers and are often motivated by topics like ethics and sustainability,” Davie said.
“While these are not usually the main things they are looking for in a role, ethics and sustainability initiatives that data scientists can be involved in can help set your company apart from the competition or present worthwhile opportunities that increase satisfaction (and retention).”
Jeff Kindred, PHR, senior technical recruiter at Shelf Engine, a company that specializes in automating insights into grocery supply chains and waste, has personally seen how data science candidates are compelled to join organizations with missions they feel they can get behind.
“When I speak to candidates about why they are considering Shelf Engine as their potential next employer, I almost always hear that our mission, ‘To reduce food waste through automation,’ caught their attention. They then began to research our organization more and were very interested to learn about us and our data science opportunities.”
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4. Companies ill-prepared for data science hiring and projects
Many companies know they want and need data science talent for a variety of company projects, but several are hiring for these roles before they realize the exact scope of their projects.
When companies move forward with disorganized data goals, they’re finding that data science professionals become frustrated and quickly move toward new opportunities.
Hoch from Revenue.io shared two typical cases in which companies hire data talent before they’ve thoughtfully planned the work for them.
“One piece of feedback that I hear a lot when people are looking for new roles: they got hired to do data science at a company that was too young, and they had to do all of this other data engineering work, and it wasn’t a good fit,” Hoch said.
“The other one that I hear about a lot is big companies will try to tackle really big and hard machine learning projects where they don’t understand everything that’s required to go into it: the budget, the work, all of it.
“These data scientists will be brought in to work on really interesting-sounding projects, but the company’s just not ready to execute yet. That leaves these engineers in a tricky spot.”
Theresa Kushner, data and analytics practice lead at NTT DATA Services, an IT service management company, believes it’s not only important that the company sets clear projects and goals for data science talent, but that they also create a supportive environment where other departments understand and collaborate well on data projects.
“In today’s environment, [data scientists] also leave in search of more meaningful project work with companies that understand their capabilities,” Kushner said.
“One of the most frustrating environments for a data scientist to work in is within a company where there are few people who understand the value they can deliver. Data scientists may not always understand the businesses they support, but they do understand data and how to manipulate it to gain value.
“The data scientist relationship with the business sponsor of the work or project is a key to keeping a data scientist or to hiring one in the first place. … Overall, data scientists want meaningful work done in an understanding and supportive environment.”
Thiago da Costa, CEO of Toric, a data and business intelligence (BI) company, believes that many companies fail their data scientists when they don’t equip them with the right teams and tools to effectively manage data projects.
“A data scientist can only be productive if they are working in a team with data engineers, data analysts, software developers, ops, and other data-related roles,” da Costa said.
“Even if it’s somewhat easy to get a job, it is also easy to fail because companies are not well prepared, tooled, and organized to make this individual succeed. As a result, we are seeing a lot of people change jobs quickly and unhappy in organizations which over-hire for the role or are not prepared to make data a priority.”
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5. Rounding out data science teams for digital transformation
Whether it’s ill-prepared resources or teams that don’t include well-rounded talent, many companies are responding to inefficiencies among data science teams with new roles that keep digital transformation at the forefront.
Cindi Howson, chief data strategy officer at ThoughtSpot, a big data analytics company, believes that analytics engineers will soon come into prominence on data science teams.
“For the last few years, data science has been the craze for companies looking to capitalize on digital transformation initiatives,” Howson said.
“However, the role of the data scientist has since lost its luster in recent memory as companies have failed to operationalize models, and universities and certificate programs have churned out coders who cannot apply their learnings in the business world. Data scientists spend countless hours on the drudgery of dealing with messy, disparate data — all of which has tarnished data science’s sheen.
“This year, I expect to see the rise of a new role in the industry that replaces data scientists: the analytics engineer. Paired with the ability for transformations to be done within cloud platforms on all data, analytic engineers will be essential to controlling transformation logic and leveraging the full capabilities of the modern data stack.”
Learn more about how to establish your data science career here: Today’s Data Science Job Market