This fundamental knowledge allows machine learning engineers to understand which algorithms best address a problem and how to optimize outcomes.
Recruiting machine learning (ML) talent is different than for traditional software development. The field of artificial intelligence is so young that it can be difficult to parse candidates by their background and experience alone. Instead, hiring managers should look for certain skills and qualities that are particularly valuable for machine learning projects, many of which are highly exploratory and experimental. While there are many different artificial intelligence job titles, these are the overall qualities to look for in when hiring for AI teams.
A solid background in mathematics and statistics is helpful in traditional software engineering but is mandatory for work in machine learning. Fred Sadaghiani CTO of Sift Science, said, "We are looking primarily for people who have a principled understanding of the statistics, probabilities, and math necessary to grasp the problem. That's the foundation of this all." This fundamental knowledge allows machine learning engineers to understand which algorithms best address a problem and how to optimize outcomes.
While many graduates have the prerequisite mathematical foundation, more nuanced character traits truly distinguish top candidates. Look for individuals with an innate curiosity and creativity to excel in the field. They are the ones best able to grapple with abstract information and deduce novel ways to approach problems especially common in machine learning. According to Sadaghiani, "a good machine learning person is a curious person, is somebody who can be creative, is somebody who can take an extremely abstract unclear problem and bring to light clarity around the possibilities."
The ability to understand data and derive meaning is also useful. While data scientists are often paired with business analysts, it's essential that they also understand the applied implications of their research. Jenny Dearborn, SAP's chief learning officer, noted that "[We're not always looking for] the right answer, but what is the right question to ask. What is the insight, meaning and purpose of the analysis that was overlaid on the data?"
Machine learning research is a new field and few projects are easy. It can take many months and countless iterations to achieve accurate results. Good researchers have perseverance and a relentless drive to seek answers. Explains Cole Shiflett, head of people operations at ThoughtSpot, "we're really looking for people who have capacity; who have the commitment to having an impact in the space and engaging with the big questions around AI."
Being quick to grasp new concepts is valued in any career, but the rapid evolution of AI makes it critical in this field. Even experienced researchers and engineers must constantly uplevel their skills to stay abreast of new developments. Furthermore, employers struggling to find experienced AI talent are broadening their hiring radius to trainable recruits and implementing in-house retraining programs for existing engineers. At ThoughtSpot, said Shiflett, they are seeking candidates who "of course are interested in the AI space, but really have the ability to learn quickly and stay on the forefront."
Perhaps the most important quality to look for in a new recruit is a passion for the work you do. "We get plenty of resumes from people with talented machine learning and data science backgrounds," said Zhen Jiang, lead analytics supervisor at Ford. "What I am much more concerned about is whether they have a passion for cars and mobility." Once you've identified a pool of qualified candidates, focus on the ones that have a particular interest in your unique problems and proprietary data sets.
Finally, make sure not to make these common recruiting mistakes for AI talent. Recruiting for AI is a challenge. The number of job openings far exceeds the number of experienced candidates meaning you will have to look beyond the resume to identify potential talent. Seeking out the traits discussed here is one-way companies in the space are recognizing recruits with the capacity for machine learning success.