You’re the head of HR at a medical devices company at the cutting edge of cancer detection. The company uses artificial intelligence (AI) in its scanning technology, but needs a specialist to hone the algorithm so the AI can learn to recognize imagery patterns associated with different cancers. You look for an oncologist with a data science or computer science degree. Good luck.
Purple unicorns” are candidate profiles with a combination of skills and experiences so rare as to be practically mythical.
That candidate profile possesses a combination of skills and experiences so rare as to be practically mythical — what recruiters now call a “purple unicorn.” Rather than pursue the unattainable, consider talent with skills adjacent to those required. Those candidates can easily expand their skills to the entirety of what’s needed for the role you’re trying to fill.
It’s important to remember that you may be hiring a candidate to fill a role, but roles are essentially a bag of skills. People with the skills necessary to perform a role’s duties may never have held that specific role or a position with that specific title.
Expand the talent pool with “adjacent” skills
Certain skills are related to others in a way that might not seem obvious, but pools of skills can be expanded with “skills adjacencies.”
During a recent pathology-data analysis challenge, an astrophysicist employing the black hole theory uncovered promising insight on cancer detection. That’s an example of skills being deployed to a task — the type of skills adjacency we can map for lots of different skills.
Here’s another example: If you need someone with natural-language processing (NLP) skills (a key machine learning technique), you should maybe look for your next hire from within marketing — and here’s why.
Upskilling from adjacent skills is more efficient
Gartner TalentNeuron™ research — analyzing billions of job postings — shows that NLP skills are closely related to the skills required to be successful in Python, topic modeling or machine learning. Given the proximity, we can assume that an employee proficient in machine learning, Python or TensorFlow is more likely to learn NLP quickly than someone without those related skills — making it more efficient to upskill them even if they have no prior NLP role experience.
While these direct adjacencies offer some new opportunities to fill skills gaps within a single domain like IT, the real potential of the adjacencies approach lies in identifying and leveraging stepping-stone skills — those that bridge the gap between domains.
By understanding this connection, HR leaders can look to one part of the organization to fill open positions in another, seemingly unrelated part of the organization. Consider the NLP example.
From social listening in marketing to NLP in IT
As the figure below shows, Python is directly adjacent to NLP within the IT domain, but there is also a complementary skill set in marketing: Sentiment analysis.
Sentiment analysis bridges two discrete collections of skills, and provides a stepping stone from marketing skills to IT skills. Specifically, a marketing employee with social listening skills is more likely to be familiar with, and ideally suited for, upskilling into sentiment analysis. From there, it’s a more direct progression to NLP skilling.
By exploiting this adjacency, HR can expand its pool for upskilling and recruiting to target marketers for NLP roles, instead of looking only in the more competitive IT domain.
The original article by Lindsey Walsh, vice president at Gartner, is here. The views and opinions expressed in this article are those of the author and do not necessarily reflect those of HR&DigitalTrends. Photo credit: iStockphoto/AaronAmat