From autonomous vehicles and home assistants to facial recognition and robotics, artificial intelligence (AI) technology is booming right now as companies such as Amazon, Microsoft, Facebook, Google, and Snapchat race to gobble up the best and the brightest on the market. However, growing as an AI function/department and building state-of-the-art machine learning functionalities depends on competing with these giants and winning the war for the relatively small number of leading edge experts out there right now.
Top companies are going after machine learning talent with practical corporate experience and a track record of successfully bringing their AI innovations to life in the real world. Tech giants are poaching machine learning talent from leading universities and organizations, offering unmatched opportunities to create significantly impactful use cases and call the world’s leading technology hub, Silicon Valley, home. Given the height of the competition, the short supply of qualified candidates who fall into the category of leading edge, and the difficulty in attracting top talent, hiring machine learning talent has become a major challenge for smaller firms in 2017.
If you’re in a position to hire, here are some tips and strategies to help you overcome the challenge of building your machine learning team.
Many companies have had data analysts and data scientists for years, but it doesn’t mean they have the experience in AI or machine learning working with the leading open source tools. As they move into AI and Machine learning, they need more advanced talent with broader skill sets to help drive change and successfully break into the leading edge space.
In a lot of cases, these companies know they need to break into AI and want to do it, but don’t have the understanding of the market that’s going to allow them to attract and retain leading edge talent who can get them there. Arguably, access to truly top-tier machine learning talent may be what defines winners and losers in the battle to enter the AI space going forward.
According to an analysis conducted by Geodesic Capital of roughly 38,000 LinkedIn members fitting the profile of top-tier machine learning talent, Fortune 500 companies, including tech giants like Amazon, Google, and Apple, employ nearly a quarter of all top-tier machine learning talent available. In order to compete for the remainder, it’s important to understand what top machine learning candidates look like and what motivates them to switch jobs.
One of the greatest challenges in hiring machine learning talent is knowing what a top machine learning candidate looks like and how to attract them to your organization. Even top companies are struggling to wrap their heads around what skill set they should be looking for and how to effectively sell the opportunity to compete with other companies.
Top machine learning candidates will typically have:
Understanding what motivates top machine learning talent to pursue one opportunity over another is the first step in building an effective recruitment strategy. Along with being educated at leading universities and gaining experience at top research organizations, candidates must have practical experience in machine learning on the corporate side.
Here’s what motivates machine learning talent to make moves:
Machine learning talent is looking for intellectually challenging opportunities that strike the perfect balance between doing research and translating that work into a product/functionality. According to leading research scientist Yann LeCun, many tech companies have trouble figuring out how to do that and lack clarity when it comes to the business problem they are trying to solve. Additionally, top machine learning talent is looking for a challenge with the opportunity to materially impact the future of a business and transform an industry through the use of data for personalization, customer service and process improvement.
Corporate salaries are significantly higher and typically include a large portion of highly lucrative long term incentives (LTIP) in the form of large grants of restricted stock or options. Top-tier machine learning talent tends to be very well paid and top companies are offering competitive base salaries and bonus opportunities that can be tough to beat. However, companies that can offer significant long-term incentive in the form of restricted stock to the tune of up to 2-3 times cash upon signing compensation can create the golden handcuffs that will lock these candidates in for 3-5 years, unless a company is willing to buy them out in the short term.
Additionally, tech giants are offering opportunities that universities, research institutions and some smaller firms simply can’t match, including, significant funding, the opportunity to create innovative products that touch millions of people, and the chance to completely transform a company or industry.
Location can make or break a candidate’s decision to pursue an opportunity. In the case of machine learning talent, the strongest hub is arguably Silicon Valley and the bay area. Between Apple, Google and the two of the best AI departments at Stanford and UC Berkeley, the resources available to talent make it difficult to compete when it comes to location. However, Seattle, Boston, New York, and parts of the Midwest are emerging AI hubs fueled by researchers at local universities.
Building your brand to differentiate yourself from your competitors is another effective strategy winning the war for machine learning talent. Candidates want to work for the best organization possible, so be sure to show off your company culture, market what you offer well, and use social media to your advantage to communicate that your organization provides a machine learning experience that no other company can. Ensure candidates understand they have the opportunity to completely transform a company or industry by working for your organization because top-tier machine learning talent is ambitious and wants to disrupt.
Leveraging the help of a good AI or Machine Learning recruiter is an effective way to access top talent with the highly specialized skill set needed to successfully break into the industry. A search firm or internal recruiter can also be a great way to educate candidates or overcome any assumptions or misconceptions.
For example, a candidate may have zero or extremely limited knowledge regarding a company’s involvement in machine learning. A recruiter can help your company more effectively tell its story to help machine learning candidates see that your organization is seriously invested in the space and has a fulfilling opportunity for them to pursue.
While it does require some investment, it’s worth it when you consider how fierce the competition for top machine learning talent is right now. With the wrong hire, you risk compromising the quality of your product along with the success of your entire company and its future.
About the Author:
Matthew J Schwartz is the Founder, President and CEO of MJS Executive Search with nearly two decades of experience in retained executive search. Matt’s expertise lies in bringing together key executives that exhibit passion and creativity with leading organizations in a wide range of functional areas such as Marketing, Sales, Digital, Interactive and more. From Digital and Social media to Machine Learning and AI, Matt is passionate about cutting edge technologies and is dedicated using his knowledge to help his clients remain or become leaders in their realm.
Founded in 2003, MJS Executive Search has established itself as a top retained executive search firm that identifies and places unique, hard to find executives in highly specialized roles.