Recruiting Data Executives
Recruiting data executives is critically important for virtually all technology companies and most mid-tier and large corporations. With more and more companies harnessing the power of big data and analytics, the demand for skilled data executives is unrelenting. An increasing number of companies are hiring data leaders: Chief Data Officer, Chief AI Officer, and Chief Analytics Officers. Yet recruiting data executives is so incredibly challenging it is literally making headlines, particularly where data science intersects with artificial intelligence. The New York Times continues to file reports on scarce data science talent:
Since recruiting data executives is famously challenging, companies have learned they need to invest more in the front end of executive searches. As a candidate sourcing and recruiting research firm, Intellerati helps companies that are actively recruiting data executives. We recommend that they take steps to ensure no viable candidate is undiscovered. Top data scientists and AI executives are elusive. Many avoid LinkedIn because they can. They get their jobs through their academic connection — it is an ecosystem that they trust.
We know that world well. We have been fellow travelers there for decades. Also, we know where top data science, machine learning, and artificial intelligence scientists like to gather.
Even more important, when we engage with data scientists, we can talk the talk. We know how to think strategically about data. We understand data science because we were early adopters. As the company’s founder, I have used data to uncover wrongdoing that couldn’t be proven any other way except by combining databases. I have witnessed the power of data in action and remain in utter awe.
What to Look for when Recruiting Data Executives
Senior executives with data science expertise know how to think about data. They understand what amazing things can be done with all that information. They harness analytics to inform decisions around how often to contact each kind of customer and what “channel” to use for each type.
For example, they know whether it is better to reach out on Facebook or by email. They spot patterns in customer or user behavior. They understand how to leverage the time of day customers are more likely to log in to a website or to play a game. Ultimately, they harness the power of data to acquire, engage and retain customers.
Where Data Science Sits
The Data Science, Analytics, Machine Learning, or Artificial Intelligence function often sits between Marketing and Engineering — serving as a kind of translator. Alternatively, it is at times seated in Marketing. Alternatively, it may be seated within Engineering at technology companies or scattered across large companies wherever data is used. Wherever data science resides, the scope of responsibilities varies depending on the organizational needs and structure of each particular company. Consequently, employers in pursuit of data leadership talent are seeking a very nuanced blend of knowledge, skills, and abilities.
What to Look for When Recruiting Data Executives
1) Can this person not only think, but do? While the role requires someone who is pretty technical and highly educated, most of our clients require that the candidate not be all up “in their head”. In fact, some clients cite that as the reason they prefer candidates with a master’s degree, such as an MBA, over a Ph.D. In addition to ideation, a candidate must have the executive ability to formulate a strategy and execute against that plan. Moreover, most clients are seeking a balance of technical knowledge with an intuitive sense of the customer, one that is informed by a background in the marketing discipline. In order to translate data analytics into ROI, you need a balance of technical ability with real human insight.
2) A proven track record of success When considering candidates ask yourself, “Has this person done what you need this person to do successfully in the past?” As you speak with the executive, get specific examples describing how they have solved the problems your organization is looking to solve. For example, if your organization is looking for someone that can increase new customer acquisition, make sure that the candidate has solid examples (with metrics if possible) of how they are doing that in their current roles or in their past roles. Of course, past performance is not always an accurate predictor of future performance, but it does speak to a person’s character.
3) Where has your candidate worked previously? Does the candidate you are considering come from an academy company that is well known for cultivating great talent? Often top data candidates are alumni of academy companies — major corporations with large data sets that help them master the craft. A major motivator? Gifted Ph.D.s in data science usually are drawn to companies that enable them to do “cool stuff”.
4) Does the candidate have startup experience? If your company is still early stage, you will likely need someone who has had experience working in a startup-like environment. To be successful, data executives at startups must be willing to “roll up their sleeves” and must be comfortable with ambiguity. If a candidate has not yet worked in that kind of environment, then there needs to be something entrepreneurial in the background that speaks to their ability to get things done in a more rough-and-tumble environment. Conversely, Fortune 100 companies require data analytics, ML, and AI executives with large company experience, adept at eliciting the buy-in required to get things done.
5) Is the candidate aligned with the company culture? The data leader’s fit with your company culture is almost as important as the candidate’s knowledge, skills, and abilities. If your culture is work-hard-play-hard, then the candidate should have a history of success working in that kind of environment. That leader may not be the best fit for a larger company that moves more slowly, is more staid and formal, or where executives don’t clock as many hours.
Of course, these are not all of the points you should consider as you conduct your next executive search for data science leadership. For more tips, check out our Executive Research Blog.