Reviewing Job Seekers

Objective

After completing this lesson, you will be able to review Job Seekers and potential matches, including through the use of machine learning capabilities.

Job Seekers

When suppliers receive 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. Once a Job Seeker is submitted, it is the responsibility of the hiring manager and/or buyer's PMO office to conduct a review of the candidates to determine their suitability for the posted position.

A Job Posting displaying the contents of the Job Seeker tab. Two workers are listed, along with the overview details of each worker, including the date and time submitted, the job seeker's name, flags, states, supplier, site, rates, units of measure, negotiated rate, currency, ratings, and whether a resume is attached. There are buttons above the list that allow hiring managers to Compare, Select for Hire, Hire, Quick Hire, Shortlist, Unshortlist, Mark as Interviewed, Schedule an Interview, Email Resumes/CV and Reject a selected job seeker.

Using Machine Learning to Review Job Seekers and Potential Matches

The buyer can review each submitted candidate individually, but when a large number of job seekers are submitted, reviewing them can be a time-consuming task.

SAP Fieldglass can use Machine Learning capabilities to parse all resumes for Job Seekers submitted to a given Job Posting. This 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.

Note

The machine learning capabilities of SAP Fieldglass are not available by default. They must be specifically enabled in the tenant.

Assessed resumes are assigned score or "assessment" based on a number of factors, including closeness to keywords provided in the job description. This functionality displays a score banding system to reflect the assessment values. 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 portion of a job posting Job Seekers tab, highlighting the Resume Assessment column in the Job Seeker list. The column contains a green Great Match label, a green Better Match label, a blue Good Match label, an orange Weak Match label, and a red No Match label for respective job seekers.

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
Qualifications.

Each category also contains two sub-categories:

Must Have
Nice to Have.

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. With this feature enabled, Hiring managers can quickly view a candidate's strengths and determine whether they are a good fit for the job posting.

Note

The algorithm used for these processes 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. 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.

Use Machine Learning Capabilities to Review Job Seekers

Melanie, the manager of the finance department at WorkingNet Networking, Inc., a global manufacturer of data networking equipment, created a job posting to procure an accounting clerk for a six-month period at their Atlanta office. The job posting was approved by WorkingNet’s PMO and other necessary approvers and was distributed to applicable suppliers, who have submitted job seekers for review and consideration. The PMO reviewed the job seekers and shortlisted them for Melanie to review.

Follow along as Melanie reviews job seekers using the resume assessment and skills highlighting results features.

ExerciseStart Exercise

Restricting the View of Job Seekers

Even if an organization chooses not to enable the machine learning capabilities in SAP Fieldglass, there is still a way that an organization could ease the burden of having a hiring manager review all submitted job seekers: it could restrict the view of submitted job seekers.

Play the video to learn how an organization can simplify the job seeker review process by restricting the number of candidates that a hiring manager can see.

Summary

SAP Fieldglass can be used to manage and review Job Seekers (candidates) submitted for job postings. Machine learning capabilities allow for efficient resume screening and methods can be used to restrict the view of job seekers to ease the hiring manager's burden.

Key Points:

Job Seeker Review: Buyers or hiring managers review Job Seekers submitted by suppliers for job postings.
Machine Learning Assistance: SAP Fieldglass uses machine learning to parse resumes, score, and rank candidates based on job requirements.
Resume Assessment Weighting: Buyers can define importance levels for skills, experience, and qualifications to influence scoring.
Skills Highlighting: Machine learning also highlights job seekers' skills for quick evaluation by hiring managers.
Restricting Job Seeker View: Organizations can limit the number of candidates hiring managers see by restricting views or using shortlists.

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