ResumeArchitect is a web-based application focused on resume review. The primary target audience is female job applicants, though the website contains features that will be useful for any job candidate. Our conception of ResumeArchitect was inspired by a recent study showing that privilege helps men, but not women, in the job search process as employers believe that women from higher socioeconomic backgrounds were perceived as less committed to their careers (1). This was largely due to the “female commitment penalty” where employers assume (often privileged) women will leave their careers to become stay-at-home mothers.  

The study led us to test the hypothesis that perceptions of privilege does lower the chance of female candidates, but that listing certain extracurriculars and skills might help candidates improve their chances in obtaining a job. We created a survey with three female candidates—a “flight-risk” (i.e.: privileged) one; a “flight-risk” candidate who has made adjustments to her resume (such that she appears committed to the industry and such that indicators of her privilege are reduced); and a candidate from a lower socioeconomic background. Our survey results, while potentially skewed with various inaccuracies, support our hypothesis: the lower-class candidate was preferably hired over both “privileged” candidates (61.29% and 51.72% preference for the lower-class candidate over the candidate without resume adjustments and with resume adjustments, respectively), while the “flight-risk” candidate with resume adjustments was slightly prefered to the one without any changes (77.5% of respondents would hire the candidate with resume adjustments).

Upon analyzing our qualitative data, we found that individuals do infer certain personality traits based on a candidate’s extracurricular and work experience. For example, volunteering in a homeless shelter showed respondents that the candidate was more relatable and had experience working with a diverse population of people. Additionally, we found that individuals tended to make hiring decisions based on their own experiences and firm culture. For example, an individual who was a first-generation college student found this to be desirable in candidates as well. And some respondents felt more inclined to hire a candidate with an extensive sports record, because they did sports in college and understand the level of multitasking needed to accomplish that. This was also true for respondents selecting candidates who they would like to work with. For example, individuals were more focused on a diversity of interests as the candidate seemed more interesting, down-to-earth, and a team player.

ResumeArchitect, thus, uses our conclusions drawn from our literature research and survey about biases in the hiring process to help women move past the initial resume screening process. The application also allows women to tailor their resume to combat gender bias when an employer reviews their resume. To use the application, the candidate only needs to input their resume, firm name, industry, and the job description of the desired position. Upon inputting this information, our proprietary algorithm uses machine learning to provide the following deliverables:

  • A short summary report on the firm culture and helpful information for interview preparation
  • A resume review, with highlighted text in red, green, and yellow. Green are resume points which we strongly encourage and indicate the strength of the applicant. Red items are problem areas, where ResumeArchitect provides suggestions on how to change. Yellow items are cautionary areas.

  • Suggestions based on the industry, firm culture, and problem areas identified in the resume scan (i.e., key words, key activities the employer favors, etc.).

The simple method used to obtain a wide array of information will hopefully increase motivation and ability factors for the user, thus making the platform more desirable. Additionally, we found that employers often use Applicant Tracking System (ATS) to sift through resumes, so there is a huge appeal toward using a resume scanner to review a resume before submission—the platform will also allow the user to realize whether or not they are presenting themselves as a good fit for the job and curate a resume that will move past the resume bots.

To explain ResumeArchitect in more detail, we’ve created a user journey: In this journey, we are following Alice, who is deemed as a potential flight-risk applicant due to her privileged background. Alice is a 2015 graduate applying to a financial analyst position at Goldman Sachs. She visits and creates an account with the website by connecting it to her LinkedIn. She then progresses as follows:

  • Upon connecting to the website, she immediate lands onto a “Scan Page,” where she does the following:
    • Copy/paste her resume into the text box
    • Includes employer name: Goldman Sachs
    • Includes industry: Financial Services
    • Copy/paste job description from the listing
    • Click “Review”
  • After the algorithm reviews the resume and compares it against her information, Alice receives the following:
    • A summary report on Company Culture in the NY office at Goldman Sachs
    • A page that shows her the scanned resume and recommendations based on the scan. There, Alice discovers that she is using many buzzwords for her position, though her GPA and list of extracurriculars might be potentially problematic. ResumeArchitect provides her with advice on correcting the potentially problematic areas, using its recommendations based on its understanding of Goldman Sachs’s company culture.

    In the end, though the website does have the potential for expansion, initially the focus will be on culture-specific resume reviews.

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