5 Solar Energy Breakthroughs with Machine Learning
Intro: Machine learning in the solar energy industry The high availability of data in the energy sector makes it a...
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.