What if more people loved what they do?
What if we could indicate how to make your current staff happier, healthier and more productive? What if ‘culture fit’ was quantifiable? What if we could understand the interaction of aptitude, attitude and environment? What if we could predict untapped potential in people? Could we use that knowledge to make life better? Could we put more people to work? Could we help the unemployed and better utilize the underemployed? Could we give people jobs they love? Can we do these things? We think we can.
Viewing talent as a genome
We are taking a scientific approach to decoding and understanding candidates and employee/employer ‘fit’ at a fundamental level. Through this project we seek to understand in detail what makes a person fit in with a team, a role and a company. The Beatles were great not just because of the talented, hard-working individuals who made up the band, but the because of the subtle interactions of the group as a whole. By better understanding people at a fundamental level we will achieve a better understanding of those subtle interactions that make some teams so excellent.
Our data analysis approach Inspired by Semantic Networks, Bioinformatics, Phylogenetics, Cladistics and Music. Through this fundamental analysis we are able to define traits which are able to be classified and ultimately used to form taxonomies for talent. This approach enables new types of analysis on candidate data which lead to the ability to form models to predict candidate behavior and ultimately to predict many interesting properties, such as “culture fit”, willingness to travel or relocation, and even happiness within various companies and roles. We’re also looking to find hidden talents. Detecting non-obvious aptitudes, underemployment, even fitness to a role in the absence of direct experience.
By viewing people as organisms which survive and thrive differently in different work environments, we also view the employer or company as the environment in which the organism evolves. Using this model we see how the organisms interact with their environments. By analyzing and comparing employment histories we gain insight into these interactions which is used as feedback to adapt our predictive models. Our goal is to be able to use these models to enable us to predict the “fitness” of a person to a particular team or environment.
Where do we start?
We start by analyzing billions of data points. We push the data through algorithms and neural networks and see what happens. We tune the models and repeat. We’ve been working for years to collect enough data, and to refine our data analysis techniques enough to get to this point. We can see the patterns. We now have enough historical data where we can look at years of employment history for hundreds of millions of people. We’re also partnering with ATS/CRM vendors to collect anonymous statistics on people during the hiring process. This data allows us to see the other side of the signal. The people who weren’t selected for interviews or weren’t hired for particular jobs. Understanding who didn’t get the job is sometimes more important that who got the job. Having both sides of the picture gives us very clear signal and deep insights into what is happening in each type of job, location, employer and industry combinations and how they related to types of people. Gaining these insights is the first step to optimizing the employment world in a way that’s never been possible before. And, maybe even bringing some more joy to the world!
Interested in learning more? Email us at firstname.lastname@example.org