People who appreciate baseball stats agree: Jonny Gomes of the Boston Red Sox is a below-average player. Yet, if you pay attention to his intangibles, he looks better. Even with lousy performance data, he was credited with playing a key role last fall in helping his team win the World Series.
The Gomes example reminds me—no matter how hard people try—that you can’t reduce everything in the world to data-driven decision-making. Especially when you’re talking about evaluating people, and how they might perform for a company.
Biotech and pharma hiring managers, are you listening?
For a couple years now, I’ve been hearing biotech and pharma companies complain they can’t find enough good job candidates. At the same time, job seekers with excellent qualifications say they can’t get hired because they only check, say, 12 of the 15 or so qualification boxes on the application. One recent story in The New York Times noted that companies across multiple industries are becoming increasingly slow and picky in their hiring practices, and resorting to various gimmicks like spelling quizzes and math tests to filter the good candidates from the not-so-good. Biotech and pharma companies are the most cautious, with an average interview process that takes 29 days, the longest for any industry, according to a recent survey by the website Glassdoor.
What’s ironic here is that in the Internet age, it should be easier to match up job seekers with appropriate job openings, like with online dating. Instead, we are seeing companies craft job descriptions that no mere mortal can fulfill. Where does that leave the ambitious postdoc from academia with a great faculty advisor, a few publications, valuable expertise in something like oncology or neurology, and a positive attitude? Company X down the street may have a job that looks ideal, but oh, the candidate lacks 3-5 years of industry experience? Sorry, you just got filtered out in the online screening process.
The thing about algorithms is they can’t imagine how a talented person who lacks a few qualifications might be resourceful, and able to adapt and grow into a top performer if given a chance in the right environment.
Ellen Clark, a recruiter of senior scientists for biotech and pharma companies, said she’s noticed companies getting pickier about candidates in recent years. When they complain they can’t find talented people it’s “baloney,” she says. Often, there are talented people in academia looking for science-based jobs in industry, but companies are unwilling to consider anything but the “perfect” candidate.
“Sometimes they really want everything,” Clark says. “They want the person to walk on the moon. They really want the person who checks every single box. They want it all. I had one search, where the company wanted an MD/PhD with an oncology background AND genetics experience to bridge the gap between the research people and the clinical people. This is the kind of thing they are looking for. They are trying to find everything in one person.”
No doubt, companies have always felt like good help is hard to find. Big companies like Genentech, Genzyme, or Novartis get thousands of applications every month. Genentech alone says it gets 20,000 to 25,000 job applications a month, while it currently lists 532 job openings on its website. I don’t envy the people tasked with trying to filter through them all in a fair and efficient way. Certainly, I get why they fear making hiring mistakes, because it can be a long, painful, and toxic process to surgically remove a tumor, shall we say.
None of that excuses the kind of dysfunction that passes for business as usual in biotech and pharma hiring. Nick Corcodilos, a headhunter and job market columnist, summed up the cracks in the system when he commented on a related article I wrote last year:
We used to talk about people: chemists, biologists, scientists. Then HR started talking about “human resources,” and more recently about “assets.” A worker (at any level) is “talent.” But this game has now pushed even top executives into an incredibly reductionist view of recruiting and hiring: It’s all about database records. Renting them, buying them, searching them, filtering them, subjecting them to algorithms.
And the databases promise perfect hires, if HR will just search the records long enough and if managers will just wait patiently. Meanwhile, important work goes undone, and fantasies of “perfect candidates” yield complaints of talent shortages.
So what are job candidates supposed to do to navigate this algorithm-driven minefield? To find out, I followed up with Marie Beltran. I spoke with her almost a year ago, when she was unemployed after being laid off from a job in quality-control at Seattle-based Dendreon (NASDAQ: DNDN).
It took Beltran almost a year to find a job, but she ended up landing what sounds like a good gig as an associate scientist with Emergent Biosolutions in Seattle.
Beltran says she didn’t encounter the spelling quizzes or video games described in The New York Times article, but she did find a way around the online filters that probably would have immediately disqualified her from getting the job she got. She used an outplacement firm, got more serious about keeping her network fresh, and added some clever social media skills. (One of her tricks was to use LinkedIn regularly, and occasionally ‘like’ some of my Xconomy articles to stay in touch with me.)
“I had to get re-educated on how to approach the job hunt process. Job hunting, networking, and social media took on a new life, versus what I knew back in 2006,” Beltran said. “I did experience firsthand how important it is to know yourself and your skills set. One has to be succinct and sharp about marketing oneself, especially with recruiters. And yes, it takes a lot of patience with biotech firms. Either it goes into the abyss, processed into the ‘system’ or flat out rejection.”
I’m glad to hear that Beltran found gainful and challenging employment. It took a lot of patience and adaptability. She may not have checked every single box you could imagine for such a position, and I doubt that an algorithm would have pushed her application to the top of the stack.
But if you believe that people are more than a collection of data points, and that attitude is a crucial intangible—like with Jonny Gomes—then I suspect Beltran will be just fine. Someone had to make a subjective, human decision about her. That’s how it’s always been in the hiring game, and how it ought to be. At least, I suppose, until the machines can prove they are better at selecting people than actual people.
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