AI/ML Algorithmic Based Recommendations for Cost and Time Effective Hiring Practices

Abstract: Despite the advent of big data, predicative analytics and artificial intelligence, the $200 billion worldwide recruitment market is driven predominantly by a human/manual process that is prone to inefficiency and inaccuracy.

Bad hires cost employers nearly 30 percent of an employee’s annual earnings. While companies spend millions on recruitment advertising annually, using strategies based and past performance and little more than gut-instinct.

There is stiff competition for talent in today’s job market amidst the tight labor market and increasing expectations of job seekers. Employers are challenged to fill headcount in both a time efficient and cost-effective manner. They need much better predictive recommendations to improve their recruiting marketing spend to hire cheaper and faster.

iCIMS, the world’s leading best-in-class recruitment software provider, applies data science practices to analyze the hiring activities 75 million applicants and 288 million visitors to the career sites of more than 4,000 companies hosted on their proprietary database in 2018 alone.

Join these sessions to discuss and explore how to:

• Apply artificial intelligence/deep learning and machine learning methods to develop a recommendation engine for the best hiring practices. A variety of artificial intelligence techniques ranging from natural language processing, classification machine learning models and deep learning will be examined.
• Solve for the problems of recruiters and HR professionals using artificial intelligence and machine learning without inheriting human bias and error
• Cleanse, normalize, analyze and predict the data behind massive amounts of hiring activity

Bio: Christopher Maier is a data scientist at iCIMS, a leading provider of recruitment software solutions for global enterprise companies. Maier plays an instrumental role in producing data insights for thought leadership content for iCIMS, including the development of the iCIMS Monthly Hiring Indicator, which measures job openings and hires. He built the indicator, which provides an early and all-encompassing view of the U.S. labor market, drawing from iCIMS’ database of more than 75 million applications and 3 million jobs a year.
Maier has additional experience in the medical device and pharmaceutical industries, solving business problems as a statistician/statistical modeler at companies including Roche Molecular Systems and The Janssen Pharmaceutical Companies of Johnson & Johnson. He holds a master’s degree in Applied Statistics from the New Jersey Institute of Technology.