Donald Trump became famous for firing and hiring contestants on The Apprentice. This week, as president-elect of the United States, he is leading the most important hiring process in the world — to fill out his cabinet and appointees. Most surprising? Trump may be taking a page from Lincoln’s Team of Rivals strategy in trying to hire some of his critics. He even met with Mitt Romney this week, reportedly to consider offering his frequent critic the job of Secretary of State.
Who Trump hires will determine what his presidency is like. So it brings up the question — how do you hire the right people? That’s a complex question, but the answer begins with being able to “read” them.
This week: hiring lessons from The Chicago Cubs, Amazon, Google, and artificial intelligence.
Read widely. Read wisely.
1. The Curious Have Won by Rany Jazayerli in The Ringer (19 min)
The gist: How Cubs GM Theo Epstein used data analytics to destroy “curses” and build championship teams in Boston and Chicago.
“If you could choose to be a fan of any team for any season in the history of baseball, you would choose either the 2004 Red Sox or the 2016 Cubs…It’s really one team or the other. Somehow, the same man built both.”
2. Cubs’ Theo Epstein is Making Lightning Strike Twice by Bill Pennington in The New York Times (8 min)
The gist: Epstein says the secret to his remarkable ability to recognize talent is not just analytics but is his method of “scouting the person more than the player” and focusing on understanding an individual’s character.
“We would ask our scouts to provide three detailed examples of how these young players faced adversity on the field and responded to it, and three examples of how they faced adversity off the field. Because baseball is built on failure. The old expression is that even the best hitter fails seven out of 10 times.”
3. Why You Should Hire People Toughened by Failure, Not Those Coddled by Success by Walter Chen in Entrepreneur (8 min)
The gist: Why Jeff Bezos hired notorious failures (Webvan execs) to start AmazonFresh.
“What Bezos learned at Amazon is that “failure comes part and parcel with invention.” When you’re innovating, failure isn’t optional, it’s part of trying something that no one has ever tried before…Conventionally successful people are often those who’ve played it safe and haven’t tried to innovate. Hire people who’ve failed at doing something bold, because they’re the only ones who’ll succeed at something bold.”
4. Here’s Google’s Secret to Hiring the Best People by Laszlo Bock in Wired (13 min)
The gist: Google’s head of talent explains how the company selects people using a data-driven approach that contradicts conventional hiring practices (an excerpt from his book Work Rules!)
“Most interviews are a waste of time because 99.4 percent of the time is spent trying to confirm whatever impression the interviewer formed in the first ten seconds. ‘Tell me about yourself.’ ‘What is your greatest weakness?’ ‘What is your greatest strength?’ Worthless.”
5. Man vs. Machine: Which Makes Better Hires? by Michael Blanding in Harvard Business School Working Knowledge (6 min)
The gist: Some companies have begun relying more on computer-administered tests than human interviewers to find the best applicants. New research by Harvard Business School Assistant Professor Danielle Li and colleagues suggests that in this case, we may have to score one for the machine.
“My sense is that managers are probably doing their best to hire the people they believe will be the best candidates,” says Li. “But they are not as good at predicting that compared to an algorithm that has access to much more data on worker outcomes and has been trained to recognize these patterns.”
“The inability to envision a certain kind of person doing a certain kind of thing because you’ve never seen someone who looks like him do it before is not just a vice. It’s a luxury.
What begins as a failure of the imagination ends as a market inefficiency: when you rule out an entire class of people from doing a job simply by their appearance, you are less likely to find the best person for the job.”
- Michael Lewis, Moneyball
One of the most important skills in life is the ability to read people, to understand what they value, what makes them tick. Read people well and you will hire well, date well, and get along well in just about any situation.
So do you read people well? Do you have an eye for talent? Even if you don’t, you probably think you do. The Dunning-Kruger effect states that you need some expertise in order to assess expertise, so people who are very bad at something often mistakenly think they are good.
One way to get a more accurate read on your own ability to read people is to test yourself. Check out this test on reading expressions developed by the University of Cambridge.
Reading facial expressions is a narrow way to think about the broader topic of reading people. Many whole professions are dedicated to the task of recognizing talent. For generations, that task has been approached as an art, but today data is turning it into a science.
One of the first places it happened was in baseball. In Moneyball, Michael Lewis chronicled the remarkable success of Billy Beane’s Oakland A’s. Although Beane didn’t have enough money to compete for the most expensive players, he built winning teams through a revolutionary use of data. The old guard of baseball scouts made player picks based on how players looked, rather than by staring at the data. That approach to a lot of inefficiencies that could be exploited by someone willing to look at the numbers. In this scene from the movie, Beane’s whiz kid hire from Yale (played by Jonah Hill) explains the opportunity given by advanced player analytics.
The knock against Billy Beane’s data-first management was that he never won a championship. But that argument has crumbled in the wake of Theo Epstein’s multi-world championship success using the same approach. As Rany Jazayerli put it, “If it wasn’t clear enough when Epstein ended Boston’s title drought 12 years ago, it should be abundantly clear today: An objective, data-driven view can change the world. It can laugh at omens. It can spit in the face of curses.”
How Google Hires
This disciplined approach to assessing talent is spreading beyond baseball to Silicon Valley. Google is one of the wealthiest companies in the world, which puts it in the opposite situation of the Oakland A’s. They can afford to hire anyone and just about everyone in the world would at least consider working at Google if given the chance. But how do you hire the right people when you could literally hire anyone?
Google, being Google, studied the data. Here’s what Laszlo Bock, Google’s chief of HR, wrote about their findings:
“In 1998, Frank Schmidt and John Hunter published a meta-analysis of 85 years of research on how well assessments predict performance. They looked at 19 different assessment techniques and found that typical, unstructured job interviews were pretty bad at predicting how someone would perform once hired.
Unstructured interviews have an r2 of 0.14, meaning that they can explain only 14 percent of an employee’s performance. This is somewhat ahead of reference checks (explaining 7 percent of performance), ahead of the number of years of work experience (3 percent).”
The best predictor of performance (29 percent) is a work sample test. In this test, you give a candidate a task similar to what they would do on the real job. So Google has every programmer do some actual coding tests before hiring.
Two types of assessment are tied for being second most predictive of future performance (at 26 percent). The first are tests of general cognitive ability (think IQ test). The second are structured interviews, “where candidates are asked a consistent set of questions with clear criteria to assess the quality of responses.” Bock says unstructured interviews end up being a waste of time because they are subjective and can be discriminatory.
“Structured interviews,” Bock says, “are predictive even for jobs that are themselves unstructured. We’ve also found that they cause both candidates and interviewers to have a better experience and are perceived to be most fair.”
Despite these benefits, most companies don’t use structured interviews because they take a lot of work to develop, maintain, refresh, and manage. It’s easier to ignore the data and believe that you’ll get useful data out of less structured, informal, conversational interviews. But the data suggests you’re fooling yourself. In fact, there is some data to suggest that people ought to remove themselves from the hiring process completely.
Leave it to the machines
A group of management professors recently published the results of a study that suggests for some jobs computers do a better job screening applicants than managers. When compared with employees picked by manager discretion, employees picked solely by a computer analyzing applicant test results had more success and stayed at the company longer.
Despite the data, I feel uncomfortable with the idea of removing people from the people assessment process. It’s ironic. By definition it is inhuman.
So maybe it is confirmation bias that made me feel vindicated when I read that even Cubs GM Theo Epstein doesn’t make decisions solely based on the stats:
“If there is an Epstein formula for success, it is complex and multifaceted but also remarkably unsophisticated in one essential way. When deciding whether to add a player, Epstein focuses most of his attention on an athlete’s personal characteristics rather than just his physical abilities.
“And the thing Epstein wants to know most about any potential player is how he has handled adversity…’In the draft room, we will always spend more than half the time talking about the person rather than the player,’ Epstein said.”
The American people just completed a hiring process of our own. We’ll find out how well we did over the next four years. A leading indicator will be how well Trump hires in the next few weeks.
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