Welcome to the latest talent acquisition strategies for data science and IT jobs. How do employers attract and hire premium talent in a super tight market that’s concurrently growing at record pace? The war for talent has been going on for decades. So what’s new, right? – Well, quite a bit actually.
First it’s important to understand that the vast majority of talented workers in data science and tech are already employed, so at best they might be passively open-minded to new opportunity. Passive job seeking could comprise as much as 80% of the talent pool, some research suggests. So let’s take a closer look at approaches to reach both an active and passive talent pool as we enter 2017.
1. Employer Branding
The art and science of candidate attraction all starts with branding. Build your employer brand and tell your story through the written word or with rich media. Marketing to candidates through branding is crucial attracting premium talent. Be sure to add value to communities that have captured the engagement of job seekers.
Effective strategies should include your unique brand positioning and the perks of working for your organization. Candidates will research your company, read reviews and arrive at conclusions about what it might be like to work there similar to the way we all shop online. So it’s important your employer brand strategy is sound, and your platforms are wisely chosen for this messaging.
2. Content Marketing
Why this works is all in the content itself. Content adds value, provokes engagement and builds credibility and depth for an organization. It positions your company uniquely and opens up the frame for storytelling. Best approaches include pieces from internal business and thought leaders, which will garnish the respect and attention that you’re trying to achieve.
Content is still very much the king, and it’s probably one of the evolved approaches mentioned. News readership is experiencing staggering growth rates across the web. Writing concise and compelling content will win the attention of both active and passive job seekers. Educational and informative topics usually are best. Then promoting your content on the right platforms.
3. Niche Job Sites
Posting jobs on niche platforms is still a great strategy to tap into concentrated talent pools. With social media saturation peaking and digital distractions running rampant, posting jobs on niche sites is an excellent way to keep your recruitment advertising more targeted.
Job seekers still engage in niche communities to stay informed of trends and to search pre-curated data science jobs and IT jobs. It’s important to write compelling job descriptions and take full advantage of this overlooked opportunity to market job content.
4. Social Recruiting
Although it can be fleeting, the social platforms provide some nice opportunities to engage candidates. But remember, we live in an era of social media saturation, and it’s difficult for social strategies to become very sticky. Social sites are very strong in networking, but when it comes to engagement, they can show some weaknesses due to content saturation. If approaching social recruiting the right way, employers can effectively reach a broader audience. However, social platforms can be more limited when targeting niche audiences.
5. ATS / Internal Resources
The ATS layer in the hiring process can be a valuable asset or an obstacle depending on your software partner. Analytical metrics reveal extremely high user bail-out during the application submit process, typically. So be sure the user experience is a good one, and your software partner has been thoughtful about design and implementation to minimize bailout. If you’ve executed perfectly on the marketing and branding side of the strategy, then the last thing you want is bailout caused by an ineffective ATS.
Once you have candidate data, ATS platforms can be a tremendous asset in organizing and tracking candidates through the interview process. ATS technology has gotten better recently though, so 2017 will be an interesting time to stay informed of trends.
Channels: Data Science