Unleashing the Power of AI Machine Learning to Improve the Hiring Process Unleashing the Power of AI Machine Learning to Improve the Hiring Process
Undoubtedly, the power of machine and algorithmic driven intelligence empowered by exponentially growing data – collectively referred to as artificial intelligence (AI), riding on... Unleashing the Power of AI Machine Learning to Improve the Hiring Process

Undoubtedly, the power of machine and algorithmic driven intelligence empowered by exponentially growing data – collectively referred to as artificial intelligence (AI), riding on the cusp of cloud-based computing, has tremendously revolutionized our visions and perception beyond any bounds. Cumulatively, rapid scaling of digital technologies is driving out-of-box boundaries of imagination to an extent where human intervention to interpret or act on a decision is increasingly becoming perishable and replaceable by AI-driven models.

For instance, can you imagine knowing whether or not you will be hired even before applying for a new job? What if someone told you the top 10 desirable skillsets you must possess to secure the job you are seeking? Can you imagine receiving advice that if you were to move to a specific state or region, you may have more than a 30 percent chance of getting hired?

[Related article: Find Your Ideal Data Scientist at ODSC East 2019]

These perspectives are routinely becoming part of the conversation in the recruitment industry, given the range of challenges that job seekers face at a time when their experience matters more than ever, and the challenges that employers face in a tight labor market. This includes stiff competition to secure talent with unemployment rates at record lows, and rapidly-evolving technology requirements driving the labor market’s tug of war between supply-demand, mounting costs of good and bad hires, challenges to fill headcount in both a time-efficient and cost-effective manner, etc.

So how can this be accomplished? What software, tools, and technologies are behind it? How accurate is the outcome or prediction?

Thanks to big data and ever-growing advanced computing feasibility, we are able to transform this wealth of digital information comprising of billions of Boolean bits into interpretable insights. What essentially transpires from today’s enabling technology is our induced ability to process, mine, extract, and transform millions hiring transactions to benefit employers in filling open roles more efficiently and quickly, and benefit job candidates in securing employment in a relevant role.

[Related article: What are Some of the Best Practices for Hiring Data Scientists?]

To learn more, join us at ODSC East 2019 in Boston, where we will showcase how artificial intelligence and machine learning methods superseding human intelligence can be applied to advance recruitment processes and improve hiring strategies and results across various industries. In the session, we will discuss a range of techniques and tools including natural language processing, deep learning, neural network, regression modeling, Bayesian modeling, and support vector machine and classification models to predict whether or not a candidate is a good match for a job.

 

Dastgeer Shaikh

Dastgeer Shaikh

Dr. Dastgeer Shaikh, Ph.D., is a senior data scientist at iCIMS, a leading provider of recruitment software solutions for global enterprise companies. At iCIMS, he has actively been engaged in artificial intelligence (AI) and machine learning (ML) algorithm development work aimed at producing insights into predictive recruiting job market data to help employers make smarter, more informed hiring decisions. By means of implementation of cutting-edge technology such as TensorFlow with Keras python framework, Dr. Shaikh has developed an AI model that predicts candidates for open jobs that employers post online and suggests relevant open jobs for candidates. He has extensive experience in working with numerous data science-centric state-of-the-arts such as Natural Language Processing, Bayesian Models, Deep Learning, Neural Network, Ensemble Modeling, linear and nonlinear modeling, Data Cleansing, building python API’s for automation, time series analysis, statistics, and mathematical models. Dr. Shaikh’s interests include financial, aerospace and social-related AI/ML modeling. He has built many AI and machine learning driven models to detect transaction risks, predict space weather, social behavior, etc. Dr. Shaikh has many publications during his tenure as PhD and post-doctoral researcher in Computational Physics.

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