**Coffee is served at the in-person seminar**

A recording of the talk will be posted afterwards on our YouTube channel at https://www.youtube.com/channel/UCN0kf0sI01-FXPZdWAA-uMA

Title: Candidates Rediscovery through Job Requisitions Matching - A Data Science POC at Workday

Abstract:
This talk aims to walk the audience through a Data Science prototyping pipeline developed at Workday for the Candidates Rediscovery use case. Talent sourcing is a time-consuming & often futile process for many recruiters. The pipeline addresses this pain point by surfaces selected, active candidates who applied for past similar job openings. Algorithms include TF-IDF, Doc2Vec, & Shingling. The audience will also get a glimpse into Workday's Machine Learning standard operating procedure from ideation to productization.

Bio:
Katharina Huang is a Data Scientist for the Machine Learning Organization within Workday. After receiving her MS in Biostatistics in 2010, she served as a survey operations specialist at the University of Michigan, using statistical methods to optimize operational timelines & sampling designs of national K-12 surveys. Katie pivoted into the tech industry in 2016 & attended the Metis Data Science Bootcamp. Since then, she has worked on NLP techniques for human capital management & anomaly detection, retail sales prediction with parallel computing, & computer vision deep learning.