Mathfi-HumanResources
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A new AI-powered service helping Human Resources platforms, HR departments or their decision experts make decisions by performing binary or multi-class predictions and classifications for example or sanity checking their existing predictions/classifications. Some examples:
1. Predicting if a job candidate will be successful in your firm: Considering staffs’ data features such as their educational qualifications, their age and demographics, number of holidays taken, their general attitude, their responsiveness to the company’s learning and development program, their growth through the ranks, their salary, their answers to certain interview questions. number of LinkedIn connections, number of times promoted, the pay rises, number of their years with business, the companies they worked for in past (before joining current firm), years of experience, the vertical they have engaged with in past, their credit rating, their current location. and many other potentially relevant data features and knowing whether those past staff have been successful or not, predict whether a potential joining candidate will be a successful staff member and with what probability.
2. Predicting if a staff member will churn and leave your firm: Considering the history of staffs who have shown churn (i.e. left the firm) or not shown churn, the department, they worked for, the rewards and bonuses received, their line managers, the number of people left within that department (i.e., department turnover) and their details, years of experience, which verticals they have engaged with in past, their number of years with business, their feedback score (from other peers and colleagues). their salary, their location, their answers to certain interview questions, number of their years with business, number of times promoted, the pay rises, the companies they worked for in past (before joining current firm), years of experience, the vertical they have engaged with in past, their credit rating, their age and demographics, number of holidays taken. number of LinkedIn connections and many other potentially relevant data features, decide/predict whether a certain current or future employee will also churn and leave the firm during next year and with what probability.
and any other binary or multi-class Human Resources classification or prediction use cases.
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