A former Netflix exec has a simple algorithm for figuring out whether you should leave your job
- Patty McCord is a former chief talent officer at Netflix. Now she runs her own consulting business.
- McCord's new book is "Powerful: Building a Culture of Freedom and Responsibility."
- She devised an algorithm for figuring out if your job is a good fit: you're doing what you love to do, what you're good at, and what the company needs.
- Deciding when to quit your job could come down to answering a few questions.
"When should I quit my job - or should I just stay?" is the kind of career question you can debate forever.
The pay is great, but your work is boring, but your coworkers are nice, but your boss is a demon… And so on.
Patty McCord has a potential solution.
McCord was chief talent officer at Netflix; now she runs her own consulting business. She's just published a book titled "Powerful: Building a Culture of Freedom and Responsibility," in which she describes best practices for people management.
Toward the end of the book, McCord presents an algorithm that managers can use to determine whether an employee is a good fit at the company. Individual employees can use the same algorithm to figure out if - or when - it's time to move on. Here it is:
Is this what the person loves to do, that they're extraordinarily good at doing, something we need someone to be great at?
I spoke with McCord and she described three potential scenarios - or outputs of the algorithm, if you will.
Sometimes, she said, "You could be doing so much more and there's stuff you're really passionate about and the company just doesn't care."
Other times, "You've got a job you can do; you just don't love it."
In still other cases, "You work really hard every day and you know it doesn't matter."
If any of these scenarios sound familiar, it might be time to leave.
Deciding when to quit your job depends on your life circumstances
The tricky part of using this decision-making tool is that "the circumstances around making the algorithm true change constantly," McCord said.
For example, early in your career you may want nothing more than to work 24/7 on something you're passionate about. When you get older, you might have other responsibilities that need your attention; "you might want to have friends that are outside of the people that you work with every day."
In that case, if your company needs you to stay until 8 o'clock every night, it might not be the right gig for you.
On the flip side, McCord said, if the company moves into a different business or its objectives evolve, the work you're doing may no longer be crucial. (McCord said this is the most common scenario she sees.)
The algorithm McCord devised won't necessarily spit out a yes/no answer - at least not immediately. But it's a solid way to clarify the most important components of an ideal work experience, and takes some of the stress out of making a big career decision.