When a Supplier receives a Job Posting as part of the contingent workflow, they have the opportunity to submit candidates, or what SAP Fieldglass labels Job Seekers, for the buyer to review, consider, and determine if they would like to hire against the open position. Once a Job Seeker is submitted, it is the responsibility of the Buyer, the Hiring Manager, and/or PMO office, to conduct a review of that candidate to determine their fit against the defined job requirements.
Reviewing Job Seekers
Objectives
After completing this lesson, you will be able to:
- Review Job Seekers and potential matches submitted by Suppliers.
- Explain visibility restriction functionality on Job Seekers.
Job Seekers
Machine Learning for Resume Assessment and Skills Highlighting
Machine Learning (ML) is a subset of Artificial Intelligence (AI) and describes algorithms that can be learned from data without having to be explicitly programmed. Machine learning capabilities in SAP Fieldglass help hiring managers find the best candidate for a job posting by evaluating resumes, observing professional backgrounds, and highlighting key skills. By utilizing the company configuration ‘Assess job seeker resumes using AI’, the application objectively uses SAP Fieldglass Machine Learning capabilities to parse all resumes for Job Seekers submitted to a given Job Posting, and, using upgraded algorithms, assigns a resume score or "assessment" based on a number of factors, including closeness to keywords provided in the job description. The algorithm used for this process ensures user privacy by first removing personally identifiable information (PII) from the job seeker’s resume as well as from the job description to ensure a secure, fair, and unbiased score is created.
This functionality displays a score banding system to reflect the assessment values, which can be categorized in the following manner:
- 80%+ score = Best Match - Job seeker’s skills and experience align perfectly with the job posting requirements.
- 60%+ score = Better Match - Job seeker is a good fit, but there may be some skills or experience that is lacking.
40%+ score = Good Match - Some skills and experience match the job description, but there are several requirements that the job seeker does not meet.
20%+ score = Weak Match - Job Seeker is lacking a majority of the job posting’s requirements.
0%+ score = No Match - Job seeker’s resume does not have any necessary qualifications needed for the job.
Not Assessed - No resume was present, or no assessment was configured or completed.
The calculated assessments are shown on the Job Seeker list view, as well as on the individual seeker cards with colored labels and bands for easy reference and can also be filtered by users looking to only see assessments of a certain level or higher.
A button entitled About Resume Assessment is also available within the Job Seeker modal window that further explains this functionality and the safeguards utilized by SAP Fieldglass as part of this AI enhancement.
Resume Assessment Weighting can be further defined within the Contingent Type to indicate the level of importance the buyer would like to put on each skill. There are four main categories that the weighting can reflect:
- Skills, Industry Experience Work Experience, and Qualifications.
Each category also contains two sub-categories:
- Must Have and Nice to Have.
This produces a true banded assessment based on the selected settings and weights for the submitted resumes. In the case where a Job Posting does not have any defined qualifications, the defined assessment weighting on the Contingent type will act as the measurement factor for the job seeker and resume.
Machine Learning provides unmatched efficiency in the hiring process by evaluating how closely a resume matches a job's requirements in comparison to other resumes being considered, accelerating the screening process, guaranteeing that candidate ranking is objective, while reducing the possibility of missing strong potential job seekers.
In addition to the Resume Assessment, a complimentary Skills Highlighting feature in SAP Fieldglass also uses machine learning to detect and clearly highlight the skills on a job seeker's resume. By enabling the company configuration 'Enable Skills Extraction for Job Seekers', hiring managers can quickly view a candidate's strengths and determine whether they are a good fit for the job posting.
Let's look at how Mavis, a manager at WorkingNet, uses this assessment functionality when reviewing job seekers for a Network Engineering job posting that has been distributed to suppliers.
Review Job Seekers and Potential Matches
In our scenario, we have now created, submitted, and approved a Job Posting in SAP Fieldglass. The Job Posting has been distributed to applicable suppliers, who have submitted candidates for review and consideration. Let’s look at how that review process is completed on the Buyer side for WorkingNet, utilizing the aforementioned Resume Assessment and Skills Highlighting results, along with a Job Seeker Potential Match called out in the SAP Fieldglass application.
In addition to the machine learning-driven results, the Potential Match function flags job seekers who may match the requirements set forth in the job posting. 
Restricting the View of Job Seekers
If Mateo wanted to simplify Melanie’s workload by limiting the number of job seekers that Melanie could see, he could restrict her view of submitted job seekers so that she can focus on reviewing only the best candidates.
Play the video to learn how Mateo can limit the number of job seekers that Melanie sees.
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