Data science teams have a talent supply problem. As companies rapidly digitize and as data strategies mature, the demand for data scientists is growing faster than the jobs market can respond.
This trend is not slowing down, it seems. Research from tech job-finder Dice shows that demand for data scientists rose by an average of 50% across several key sectors during 2020.
In our monthly panel, broadcast live on LinkedIn, six leading female data and analytics executives discuss how they are addressing the talent shortage in their organizations, strategies to nurture the talent you already have, and the importance of inclusivity to data and analytics teams.
“Variety and different perspectives of all types is really going to be important if we're going to design inclusive products and inclusive services as we all go forward into the next generation of the information age,” says Mastercard Chief Data Officer JoAnn Stonier. “It's a hard thing to recruit for, but it's also something that we need to train on as the next generation is developing right in front of us right now.”
Of course, recruiting talent using agencies, social media platforms and by advertising online will often be the first approach. However, the panel suggests that some ‘out of the box’ thinking can also produce results.
“We [reach talent] through things like webinars and online seminars for graduates to come and spend the afternoon with one of our analysts,” says Openreach Director of Data and Analytics Nirali Patel. So, we’re trying different mediums and moving away from your traditional advertising.”
Internal recruitment is another strategy to identify new data and analytics talent, especially for staff who are interested in moving their career in a new direction.
“We are focused on growing talent within our organization, and that’s not only people in the data and analytics teams but also people outside of the data analytics teams who are interested in data and analytics and want to pursue it as a career,” says Maritza Curry, Head of Data at RCS Bank. We then use secondments etcetera to facilitate that.”
Clearly, strong data science candidates should have significant technical skills. However, the panel was keen to highlight the importance of soft skills in modern data science teams.
“You're really looking for really strong communication skills and really good business thinking because an algorithm is only as good as [our ability to] apply it to a real-world business problem,” says Dr Amy Gershkoff Bolles, Bitly CDO and GM of BitlyIQ.
She continues: “I always look to ensure that our talent has a breadth of skills at every level. The engineer who's working on a problem also needs strong business thinking and communication skills, not only the executive who's leading the team.”
“We talk a lot about data science skillsets and analytic skills,” adds Stonier. But soft skills and the ability to understand data in context help us understand how to translate a business strategy into a data strategy.”
For Stonier, strong soft skills are also essential for teams to grapple successfully with the deeper impacts of the work of data and analytics teams.
Do we have the other types of skills that we need for the ethical questions that go along with our data age? Do we make sure that we understand the impacts of the work we're doing?” Stonier asks. “This goes to who we hire so that we understand how we're forming our data questions, are we getting enough variety in our data sets as we begin to apply AI and machine learning.”