Data Science in Human Resources

Internet technologies and the increasing mobility of people have made the hiring process very competitive. Companies spend an average of 23.7 days on hiring a new employee, while the best candidates stay on the market for only ten days. On the other hand, many companies avoid hiring from popular job websites. Posting a vacancy on LinkedIn or Indeed may bring in thousands of resumes from candidates. Of these applicants, most are not sufficiently qualified, especially for technical positions. So, HR teams have three important goals to meet during the hiring phase: to make the hiring process faster, to attract more talent, and to analyze more resumes.

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Predictive analytics technologies and natural language processing help HR teams to filter through thousands of resumes, focus on the best channels for hiring and create a base of the most promising candidates on a specific market. The main advantages of using data science in hiring are (a) a decrease in hiring time and (b) an increase in candidate quality for the company. Predictive hiring in the recruitment process is successfully used by big companies. For example, the implementation of a predictive hiring tool in Wells Fargo increased retention rates for tellers by 15%.

Predictive analysis can increase employee engagement and productivity. Even if a company has successfully gained a good employee, this is only the beginning of a longer process. The candidate should be introduced to their new duties and involved in professional training programs. Many companies have realized that the cost of training is higher than the profit they receive from it.

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Data science can examine employees’ learning levels and help to modify training courses, in order to make them more productive. Using Big data analysis, companies can also detect potential mistakes and flaws in work. For example, JetBlue wanted to improve on training attrition with new members joining the team. To accomplish this, JetBlue used data science methods in their analysis. As a result, training failure-based attrition fell by 75%, and overall training attrition fell by 25%.

One of the biggest challenges companies face is staff retention. In general, companies invest money and resources in hiring new people, and then allocate 1-3 months to training new employees. This investment will be a loss if a person decides to leave within their first six months of employment. Employees may become dissatisfied due to a variety of reasons: low salary and poor communication with superiors are just two examples of many impacting factors. Employers can never be sure that the members of a “strong team” will be staying at the company for the foreseeable future. One third of employees at companies with 100 plus employees are currently looking for jobs. Predictive analysis can find the hidden reasons for employee dissatisfaction and allow HR management to find solutions before the main problems come to a head. Machine learning techniques scan employees’ attributes to detect increases or decreases in resignations

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Besides retention, there is also employee turnover. This term refers to the percentage of workers who leave an organization and are replaced by new employees. There are many factors which affect turnover in a particular organization. Some of them can be easily resolved, but some present more of a challenge. In the second case, predictive analytics uses historical data to predict how many employees will leave the company and when they will do so. Thus, the HR team can start looking for new candidates before old employees resign.

Finally, it is worth noting that big data analytics have become the new force for contemporary HR management. New data science approaches decrease the time and resources required to hire better workers. These approaches also increase employee engagement, their productivity, and retention rates. While the human factor in HR work is very important, the implementation of machine learning techniques in HR teams increases their productivity and eliminates routine.