4 Novel ways to build data science skills in-house🎓

Recruiting data science talent has been one of the topmost challenges for data leaders over the past several years. Thanks to the Great Resignation, we can now add talent retention to this list of top challenges.

It’s actually not the Great Resignation, but the Great Upgrade, says a witty tweet.

People tend to stay at companies when they’re still learning. Upskilling can be the perfect antidote to the Great Resignation for smart companies.

If there is a way to upskill your internal talent into data science *and* retain them in the process, this may be an attractive option after all.

No, building data science talent internally isn’t terribly difficult. Here are 4 ways you can do this in your organization:

1. Look for talent beyond your IT team

“Every organization is underutilizing their current staff due to a lack of awareness,” says Lisa Palmer, chief technical advisor at Splunk. Teams often restrict their internal search to technology teams. “You’d be surprised by the versatility and depth of talent available outside IT, in your lines of business,” she adds.

To discover the gems hidden across your organization, maintain a self-identified list of skills for every employee. Update the list every six months and make it openly searchable by associates. Palmer recommends self-classifying each individual’s skills into four categories: expert, functioning, novice, and desired stretch assignment.

2. Curate your data science curriculum using public content

Finding the right content to upskill your in-house teams is a challenge. Despite the rapid mushrooming of training portals and MOOCs (massive open online courses), the curriculums may not meet your organization’s specific needs. With rich materials available online, often for free, it’s a bad idea to recreate your own content.

“You must design your own curriculum by curating content from multiple online sources,” says Wendy Zhang, who was the director of data governance and data strategy at Sallie Mae. Customize the training plan on your team’s background and roles to get the best of both worlds – content reuse and flexibility to suit your needs.

3. Upskill your analytics team on domain expertise

Good AI solutions need the right combination of domain and technical expertise. People who go through the upskilling are often siloed in their perspectives. Technical training often fails to provide exposure to business applications, while business orientations aren’t grounded in technology.

“To address the challenge, we created an Agile routine called Learning Days,” says Todd James, who was the SVP of Intelligent Automation at Fidelity Investments. This provided a platform for the data scientists to educate business teams on AI use-case identification using practical examples. The data science teams, in turn, received briefs from business partners on strategy, products, and business processes.”

4. Enable application of new skills by experimenting on the job

To paraphrase Julius Caesar, experience is the best teacher. You internalize any new skill only when you apply it in practice. The best courses will amount to nothing if you don’t let your teams experiment, make mistakes, and learn on the job.

When internal candidates have a growth mindset and an aptitude to learn, design on-the-job training. Pair up novices with more experienced employees and set clear expectations for the shadowing period. “Define beginner tasks that the novice can take on immediately and create laddered tasks as they gain proficiency,” adds Palmer.

This is an excerpt of my article published in The Enterprisers Project.