Pymetrics is a company that uses games and data science algorithms to assess cognitive and personality traits in order to improve the efficiency and outcomes of the hiring process. Their tools can be used for sourcing, sorting, screening, and internal mobility. Their plan involves collecting data from existing employees who play games, building models based on top performers, implementing games for applicants to receive trait reports and fit percentages, and integrating the tools into clients' hiring processes. The company was co-founded by two female neuroscientists and has received funding from investors like Khosla Ventures.

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industry challenge
about pymetrics
our tools
our plan
our team
contents

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industry challenge the hiring process is inefficient, often requiring
250 applicants to yield 1 hire
the hiring process is not predictive of success,
with 30-50% of hires failing in their first year
the hiring process is biased resulting in reduced
diversity and increased compliance risk
the hiring process is subjective rather than relying
on objective measures

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about pymetrics

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the company
Investors: Khosla Ventures, Mercer, Randstad
co-founded by two female neuroscientists
recognition from FInTech Innovation Lab,
Work-Bench Enterprise Technology Accelerator

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pymetrics solution pymetrics customizes algorithms by company
and role to further improve efficiency
pymetrics uses data science algorithms to
maximize prediction of employee success
pymetrics uses only bias-free algorithms to
reduce and mitigate bias present in all humans
pymetrics uses objective data collected through
measuring performance on games

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we assess cognitive and personality
traits using behavior on games
the games have been used for
decades in neuroscience research
they collect more, and more accurate,
data than questionnaires because they
directly measure behavior rather than
relying on self-report
games = objective inputs
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scientific games

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trait reporting
with the game data, we develop
individualized profiles for each user,
reporting on 90 different cognitive
and personality traits
trait reporting = objective outputs

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traits
our games assess over 90 cognitive and personality traits such as
cognitive
processing speed
continuous attention
impulsivity
working memory
memory span
cognitive flexibility
pattern recognition
ability to avoid distraction
cognitive control
planning
sequencing
learning
risk profile
reward sensitivity
perseverance
emotional sensitivity
emotional identification
creativity
trust
altruism
tolerance for ambiguity
ability to delay gratification
learning from feedback
learning from mistakes
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personality

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data science
data science beats linear models
data science uses non-linear modeling
and iteration to find complex
relationships linear models miss
data science predicts future
performance while linear models
describe past relationships
data science = 200% better prediction

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custom algorithms
roles and organizations are unique so
we build custom algorithms for specific
roles and companies
company employees play the games.
their data is used to build custom
algorithms of what success looks like in
a particular role at a given company
customization = 5-10x more efficiency

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bias-free algorithms
we guarantee that our algorithms
are free of gender and ethnic bias
our guarantee stems directly from
our large technology advantage
our technology advantage rests
on more, and more accurate,
data, and our use of data science
not linear models
algorithms = no unconscious bias

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objective data creates an objective
process, leading to increased employee fit
a 200% + reduction in candidate failure
rates, going from 30-50% to 10-15%.
reducing candidates reviewed from 250 : 1
to 25 -50: 1, leading to 5-10x increased
efficiency
gender and ethnic bias-free algorithms lead
to increased diversity and decreased
compliance risk
benefits

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much greater recruiting efficiencies
compared to resume review, top
consulting and financial service firms
saw:
6x improvement of applicant : offer
yield : from 150:1 to 25:1
3x improvement of interview : offer
yield: from 8.5% to 25%
3x reduction of false negatives: from
30% to 10%

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reduced attrition for first-year hires
typical attrition rates for first year hires is
between 20-30%. two firms, in financial
and consulting industries, reduced
their attrition by an average of 50%
consulting: reduction from 25% to 10%
finance: reduction from 30% to 20%

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greater diversity of applicants
a top-tier financial service firm used
pymetrics to drastically increase
gender diversity of applicants
typical applicant pool for equity
research position is 75% male
pymetrics applicant flow gender-
equal (51% male, 49% female) for
the first time ever

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quantification of culture fit
ability to provide culture fit profiles
for different firms who hire into the
exact same role
culture fit profiles then predict offer
acceptance rate : people who
accepted an offer had 2x the
culture fit score than people who
declined a firms offer

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our tools
sourcing
sorting
screening
internal mobility

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tools
Employees within a company complete
the pymetrics suite of games. They are
sorted into various internal positions
according to their fit scores based on
predictive fit, which is gauged by our
customized algorithms.
internal mobility
Screening assessment is the traditional
application of testing. Companies
administer the pymetrics games to all
applicants, we in turn, provide fit scores
for all applicants based on a
customized algorithm. The algorithm is
constructed using a data-driven
approach and all contributing factors
are cross validated. Historically,
companies have used employed
screening assessments at either the top
or middle of the funnel to aide in the
hiring process and sift candidates. The
technology can be used to assess role
fit, or company fit more broadly.
screening
pymetrics integrates into the front end
of a company’s job site, so candidates
play the games as the earliest step in
the application process. Upon
completing the games, applicants are
guided to apply highlighted roles on
the company website based on the
results of their trait report and fit with
open roles. Candidate experience is
improved through guidance, and
recruiters are able to more easily
consider candidates for multiple roles.
sorting
pymetrics has a job marketplace,
powered by our technology, to match
candidates with companies. pymetrics
markets directly to job-seeking
individuals, who go through the
pymetrics assessment optionally, not as
part of a company recruiting process.
These individuals are then matched
with company recruiters if they are a
high fit for the role that a company is
hiring for based on our algorithms. Only
top-fit candidates are sent to
companies.
sourcing

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our plan

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data collection
Client identifies roles to
model and existing
employees to play games
communications rollout
employees play all 12
pymetrics games
1
model building
pymetrics analyzes data
from benchmark group
pymetrics crafts models off
of top performers in each
role
pymetrics delivers model
and reporting
2 3
implementation
Applicants play games
and receive trait reports
Candidates are sifted
based on percentage
match
Applicants whose fit
exceeds threshold
move to next phase of
process
4
process integration
pymetrics completes
technical integration
into client jobs site
plan overview

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our team

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our team
founders
frida polli,
PhD, MBA
julie yoo, PhD
legal
avital garner-
samet, JD
data science
matt malter
cohen, PhD
evan blumgart,
MS
katy ghantous,
PhD
melissa louie,
PhD
darshana
narayanan, PhD
mark ward, MS
enterprise
sales
marketing +
product
jessica almog
jessica barrett,
MBA
lauren cohen,
MBA
andrew arvin
jim kernan,
MBA
joe naso
engineering
fedor garin
naveed
jooma, MS
dennis lin
zachary smith,
MS
john salvatore
investors

24. contacts
frida polli, founder + CEO
frida.polli@pymetrics.com
jess almog, business development
jessica.almog@pymetrics.com
jessica barrett, business development
jessica.barrett@pymetrics.com
lauren cohen, business development
lauren@pymetrics.com
www.pymetrics.com