MeVitae is on a mission to enhancing human intelligence by compensating on our brain’s limitations. The first application of the technology is focusing on leveraging data-driven cognitive solutions to solve the world’s biggest employment challenges, from increasing workplace diversity to global mobility. The initial focus is helping companies hire smarter, faster and fairer without cognitive and algorithmic bias.
When shortlisting applications, recruiters typically spend seven seconds on each CV. In this time, they look at a candidate’s name, schools, maybe their current position, and not much else. The data show recruiters make sub-optimal decisions that are strongly impacted by unconscious biases.
The successful candidate will join the technical team to work on:
(1) Fitting generative models of the shortlisting process to data
(2) Developing and improving our algorithms that compare CVs to job descriptions
(3) Use non-parametric techniques to shortlist CVs based on CV/job-description comparisons.
This project will make use a range of techniques including Bayesian parametric and non-parametric methods.
A PGR with a firm grasp of Python and statistics, including Bayesian methods. Good communication skills to explain complex ideas. Ideally, but not essentially, experience fitting generative models to data using packages such as PyMC or PyStan and experience with Scipy and Scikit-learn.
If you would like further details, please contact me at email@example.com