The war for talent is raging across industries and countries, and nowhere is the fight more intense than in data science and analytics. In fact, Gartner anticipates that there will be a shortage of 100,000 data scientists in the United States alone by 2020.
The reality: traditional HR struggles to fulfill the analytics remit
Even the most apt HR teams are already struggling to fulfill their analytics remit. They rely on traditional approaches to HR that don’t resonate with this unique set of experts. According to a recent survey of analytics professionals, these approaches can create a number of barriers to success, including:
1. Negative first impressions. Analytics talent, especially recent graduates, expect to work in dynamic organizations. Put them through a long-winded, paper-based hiring process, and you can be sure to create a negative first impression. Moreover, in the analytics community, word-of-mouth matters.
2. Narrow sourcing. A brand that resonates with traditional job applicants doesn’t necessarily enjoy the same profile within the analytics community. This can pose a challenge for recruiting analytics talent.
3. Imbalance between monetary and non-monetary rewards. A US consumer goods company recently learned that its incentive package is popular in core business and IT areas, but not among analytics talent; specifically, retention bonuses were less effective.
4. Unclear career tracks. Analytics talent cite “lack of a growth trajectory” as a primary reason for leaving their previous employer, according to the above survey.
The fix: take inspiration from leading analytics organizations
To win the talent race, traditional HR approaches for hiring, retaining and developing talent must be replaced by customized approaches for this unique audience, including:
· Use recruiters that specialize in analytics – or, even better, hire recruiters that are former analysts. In 2015, a mobile gaming company hired an ex-Wall Street quant to manage analytics recruiting. Because of her prior analytics experience, she knew how to screen candidates effectively. She also understood what matters to top candidates, and was thus able to present analytics at the organization in the best light. In 18 months, she more than doubled the size of the team.
· Practice what you preach – by using analytics within the HR function, including candidate profiling, attrition prediction, and workforce planning. Google applies predictive analytics to optimize its hiring process end-to-end, from optimizing the number of interviews per candidate to predicting which candidates have the highest probability of succeeding after being hired. eBay tracks not only metrics around hiring processes, but also analyzes promotion, retention, and compensation data to ensure optimal talent management.
· Hire analytics leaders – with the optimal balance of technical, communication, interpersonal, and managerial skills. Soft skills are important, not only because leaders tend to hire people who resemble themselves, but also because they’ll need to be able to forge connections with the wider business.
· Proactively source diverse candidates –HR teams should consider sourcing from non-traditional candidate pools, for instance, by partnering with organizations that support under-represented groups. In addition, it can be beneficial to source candidates from diverse academic and technical backgrounds; for example, a retailer sourced computational biologists, nuclear engineers, and physicists for data science roles, rather than exclusively sourcing data scientists from other technology companies.
· Develop a learning culture – Consider that, in the US, 86% of all workers would invest free time to learn new skills. In our experience, analysts tend to be even hungrier for new knowledge. They aim to continuously learn new skills, from the latest external trainings on statistical algorithm or data visualization, to internal trainings on business processes. For example, Zynga created a data science apprenticeship program, wherein new hires take several online data science courses, complete a series of data science projects under the supervision of a senior data scientist, and at the end of the 18-month program, join the data science team.
These examples show how organizations and their HR teams can successfully attract scarce analytics talent. Data scientists have special requirements and know their value. Organizations need to acknowledge this and act decisively upon it if they want to stand a chance at recruiting the best and brightest – and capturing new business value, as a result.