Notes from the #BigDataTO conference in Toronto
- He realized that HR teams were spending too much time prescreening resumes before they could even meet with the best candidates
- Recruiters only spend 6 seconds reviewing a resume which means they end up accidentally discarding some of the best ones. Time crunches mean they may only be able to get through 20% of candidates. ML can solve these problems .
- 75% of candidates who apply to jobs do not hear back from the company because there are simply too many candidates and not enough time to do so. NLP and chatbots can solve this problem.
- AI will not steal all jobs but it will automate processes and allow you to engage with potential hires in a more meaningful way.
- Shortlisting is a huge challenge for HR as reducing a huge list of resumes into a screened list takes a lot of detailed attention. Technology such as direct keyword matches aren’t the best option as they eliminate people with relevant skills but not the exact words. For instance, know R is just as good as knowing SAS but a keyword search wouldn’t know that. NLP would work much better.
- Personality insights can also be collected using sentiment analysis to get a functional understanding of the Big 5 Personality traits. [Wow, I can’t imagine how valid it is to do personality assessments with resumes which are often written by third parties and without traditional grammar and style]
- Chatbots can take an applicant through hiring and onboarding processes by answering questions that would normally be asked of an operations officer. [imagine how many stupid questions the chatbot would be asked that new hires are too scared to ask people]