Case Study: Job No. 1 - HR driven by ML


Using ML-powered detection/prioritization will help your investigators get more applicants’ CVs thanks to NLP-based job-to-candidate matchmaking, predict employee retention issues, and identify talents.

Business case

A small number of applicants for the company’s career page leads to the payment of other sources as recruitment agencies or job boards. Additionally, only 2 to 5% of potential employers reply to the job advert on company career pages, and it is difficult to find candidates because of the small CV database, which forces recruiters to search using full-text and tags. Also, the hiring process is too long and demanding a lot of administration and manual work, that as a result company loses potential revenue and is short of employees. The only solution, in that case, could be JOB#1.


First, each CV is automatically analyzed by JOB#1 and connected to the most appropriate job and vice versa. Secondly, the CV upload button replaces the standard filter for vacancies, which within few minutes, prepares the most relevant opportunities for the candidate. And then, JOB#1 takes the full-text search SQL queries in the ATS database of CVs.

In the background, an NLP analyzes CVs and job advertising, indexing them in the semantic space. Then, the ML-powered search chooses the CVs index for the career pages and the jobs index for ATS. And on the last step, there is a filtering of the results.


• 15x more CVs as candidates insert CVs on the first step helping to build a CV database for future recruitment

• Saving costs on recruitment agencies and advertising on job boards

• Shorten time for hiring

• Recruiters get sorted candidates by relevancy for each position

• JOB#1 automatically detects suitable candidates within the organization

• Supports internal rotation of employees and decreases fluctuation

• Enhance candidate search in candidate database (ATS), replacing poorly working full-text

• Candidates do not have to fill in any forms, only upload their CV and receive a list of matching positions with the most fitting ones at the top


Milan Mahovský, mBlue | product:

“DataSentics developed a real-time Machine Learning application that can read CVs and job advertising. It significantly improves the job applicant’s experience and acquires large volumes of CVs in rapid time. This technology sorts the CVs by relevance and saves time spent on more value-added tasks for the recruiter. [As a result], more candidates were sorted and onboarded faster helped mBlue with prompt delivery of services and led to higher profitability of the company.”