Name: Using AI and Machine Learning Capabilities to Rank Resumes in SAP Fieldglass
Uploaded: 2024-9-9
Duration: 308 s
Description: This video informs users of the AI and Machine Learning functionality within the SAP Fieldglass application that aids in the important process of evaluating Job Seekers for hire.

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<div><div><div>This video informs users of the AI and Machine Learning functionality within the SAP Fieldglass application that aids in the important process of evaluating Job Seekers for hire.  </div></div></div>

Using AI and Machine Learning Capabilities to Rank Resumes in SAP Fieldglass

This is Mavis. Mavis is the Manager of Network Engineering at WorkingNet Networking, Inc., a designer and manufacturer of data networking equipment. Mavis needs a temporary worker to replace an engineer who has unexpectedly gone out on medical leave. So, naturally, she uses SAP Fieldglass to create a job posting for the open position. The job posting is sent out to five supplier partners, who in turn send back five job seekers apiece for Mavis to review. But Mavis is a busy manager, so how can she adequately review 25 job seekers to fill a position that needs to be filled immediately? Well, WorkingNet has configured its tenant of SAP Fieldglass to include features that use machine learning AI to help speed up the process of evaluating job seekers. Machine learning gives hiring managers an edge in their hiring process by evaluating how closely a resume matches a job's requirements and ranks job seekers by matching the skills they've listed on their resumes to those on the job description, generates a score for each candidate to determine how well their skills and professional experiences align to the job posting, and highlights the technical and soft skills on the candidate's resume so hiring managers can see at a glance if a candidate possesses the desired expertise. When the company configuration Rank Resumes for Jobseekers is enabled, the application uses machine learning capabilities to parse through resumes by objectively comparing its contents to keywords provided in the job posting description. Resumes with the closest match to the job posting are ranked 1, resumes with the next closest match are ranked 2, and so on. Because the algorithm rates the resume objectively, more than one job seeker can have the same rank. This feature reduces the number of job seekers Mavis has to review by listing, in order, the resumes that most closely match her needs. To view the resume ranking feature, Mavis will select the Job Seeker tab of a job posting. This displays the listing of all the job seekers who have been submitted to the position. Because the resume ranking function has been enabled, she sees a new column added to the page, resume rank. This lists the rank number assigned to the job seeker by the algorithm. Mavis can use this to quickly pick out which job seeker she wants to review. When she selects one to review, a modal window appears showing the details of that job seeker. The machine learning AI can also assess the experience of the job seeker, so hiring managers like Mavis can easily determine whether a job seeker's professional background aligns with the requirements listed on the job posting. It does this by identifying two different points of assessment. It compares the candidate's previous job titles and generates a Related Experience percentage, which is displayed in the center of the information area above the resume. The Related Experience percentage indicates how well the candidate's professional experience matches what is needed for the job. Then, as it does when it generates a ranking, it compares keywords in the candidate's resume against keywords in the job posting. If the algorithm determines that the job seeker is a good match, it will display a check mark in the Industry Match column. The skills highlighting feature uses machine learning to detect skills on a resume and highlights them, making it easy for the hiring manager to quickly view a candidate's strengths and determine whether or not they are good fit for the job. To activate this feature, Mavis would select the Resume Analysis button. This button opens a simplified resume view with all of the skills highlighted. Hard skills, or technical skills, are highlighted in blue. Soft skills are highlighted in yellow. With this feature, Mavis can easily scan to see if the job seeker possesses the skills she requires for the position. Here are a few things to consider when enabling use machine learning to rank resumes in the company configuration. The resume ranking function only applies when the supplier has attached a resume for the submitted job seeker. Job seekers whose submissions do  not have resumes attached  will not be ranked. The resume ranking functionality is not tied to any job posting template rules. As a result, the company configuration applies to all job postings for that buyer. However, if the functionality is enabled for an existing tenant, the settings apply to job seekers submitted in the previous six months as well as all job seekers going forward. The rankings are recalculated when a new job seeker is submitted to the job posting or an existing job seeker is withdrawn. To view the ranking information, the buyer user role permission called view resume rank must be enabled within the job seeker module. For the AI and machine learning capabilities to work best with the resume ranking feature, it is important to utilize commonly used job posting templates that include an up-to-date date and accurate job posting description.