The Data Race Against The Pandemic, Together
How ML and data were crucial in fighting COVID-19 in 2020 as a united global community. 2020 was a year...
A fortune 500 Hedge Fund was looking to quantify beneficial hiring characteristics and to develop predictive hiring indicators to filter candidate applications. They had 10 years of unstructured free-text, both through resumes, third-party data, and interview notes. This contained large amounts of unstructured (free text, scans, emails) data. They were looking to standardize this data for improved analysis and to reveal non-standard correlative success factors.
Vertical: Talent Analytics
Model: SVM/Random Forest
In an effort to systematically improve data standardization and quantify the hiring pipeline, we applied numerous data science techniques in two foundational aspects of the hiring pipeline.