How to make applying for jobs less painful

Applying for jobs online

is one of the worst
digital experiences of our time.

And applying for jobs in person
really isn't much better.

[The Way We Work]

Hiring as we know it
is broken on many fronts.

It's a terrible experience for people.

About 75 percent of people

who applied to jobs
using various methods in the past year

said they never heard anything back
from the employer.

And at the company level
it's not much better.

46 percent of people get fired or quit

within the first year
of starting their jobs.

It's pretty mind-blowing.

It's also bad for the economy.

For the first time in history,

we have more open jobs
than we have unemployed people,

and to me that screams
that we have a problem.

I believe that at the crux of all of this
is a single piece of paper: the résumé.

A résumé definitely has
some useful pieces in it:

what roles people have had,
computer skills,

what languages they speak,

but what it misses is
what they have the potential to do

that they might not have had
the opportunity to do in the past.

And with such a quickly changing economy
where jobs are coming online

that might require skills that nobody has,

if we only look at what someone
has done in the past,

we're not going to be able
to match people to the jobs of the future.

So this is where I think technology
can be really helpful.

You've probably seen
that algorithms have gotten pretty good

at matching people to things,

but what if we could use
that same technology

to actually help us find jobs
that we're really well-suited for?

But I know what you're thinking.

Algorithms picking your next job
sounds a little bit scary,

but there is one thing that has been shown

to be really predictive
of someone's future success in a job,

and that's what's called
a multimeasure test.

Multimeasure tests
really aren't anything new,

but they used to be really expensive

and required a PhD sitting across from you

and answering lots of questions
and writing reports.

Multimeasure tests are a way

to understand someone's inherent traits --

your memory, your attentiveness.

Our vision at pymetrics
is to take multimeasure tests

and make them scalable and accessible,

and provide data to employers
about really what the traits are

of someone who can make
them a good fit for a job.

This all sounds abstract.

Let's try one of the games together.

You're about to see a flashing circle,

and your job is going to be
to clap when the circle is red

and do nothing when it's green.

[Ready?]

[Begin!]

[Green circle]

[Green circle]

[Red circle]

[Green circle]

[Red circle]

Maybe you're the type of person

who claps the millisecond
after a red circle appears.

Or maybe you're the type of person

who takes just a little bit longer
to be 100 percent sure.

Or maybe you clap on green
even though you're not supposed to.

The cool thing here is that
this isn't like a standardized test

where some people are employable
and some people aren't.

Instead it's about understanding
the fit between your characteristics

and what would make you
good a certain job.

We found that if you clap late on red
and you never clap on the green,

you might be high in attentiveness
and high in restraint.

People in that quadrant tend to be
great students, great test-takers,

great at project management or accounting.

But if you clap immediately on red
and sometimes clap on green,

that might mean that
you're more impulsive and creative,

and we've found that top-performing
salespeople often embody these traits.

The way we actually use this in hiring

is we have top performers in a role
go through neuroscience exercises

like this one.

Then we develop an algorithm

that understands what makes
those top performers unique.

And then when people apply to the job,

we're able to surface the candidates
who might be best suited for that job.

So you might be thinking
there's a danger in this.

The work world today
is not the most diverse

and if we're building algorithms
based on current top performers,

how do we make sure

that we're not just perpetuating
the biases that already exist?

For example, if we were building
an algorithm based on top performing CEOs

and use the S&P 500 as a training set,

you would actually find

that you're more likely to hire
a white man named John than any woman.

And that's the reality
of who's in those roles right now.

But technology actually poses
a really interesting opportunity.

We can create algorithms
that are more equitable

and more fair than human beings
have ever been.

Every algorithm that we put
into production has been pretested

to ensure that it doesn't favor
any gender or ethnicity.

And if there's any population
that's being overfavored,

we can actually alter the algorithm
until that's no longer true.

When we focus on the inherent
characteristics

that can make somebody
a good fit for a job,

we can transcend racism,
classism, sexism, ageism --

even good schoolism.

Our best technology and algorithms
shouldn't just be used

for helping us find our next movie binge
or new favorite Justin Bieber song.

Imagine if we could harness
the power of technology

to get real guidance
on what we should be doing

based on who we are at a deeper level.