Our Research

Resume Genius produces original research on the careers space — how people look for work, how employers hire, and how both are changing. This page explains where our data comes from and how we work with it.

We hold our research to high standards. Our findings are independent of our commercial products and are never shaped to reach a predetermined conclusion. We disclose our methodology for every study, we are open about the limitations of our data, and we publish the raw data behind our original surveys so anyone can check our work.

The Resume Genius research team

	Eva Chan, Senior Digital PR Writer Eva leads research and data-driven campaigns examining trends in the modern workforce and serves as a media spokesperson for the company. Eva’s work on job seeker behavior, hiring trends, and workplace data has been featured in press interviews and outlets covering the labor market.
	Jack Hulatt, Digital PR Specialist & Data Expert Jack Hulatt holds a bachelor’s degree in Politics and International Relations from the University of Greenwich, along with certifications in computer science and data analytics. He runs data analytics on survey results and builds the AI-assisted database that powers them.
	Geoffrey Scott, Senior Hiring Manager & Career Coach Geoff Scott is a Certified Professional Resume Writer (CPRW), helping job seekers write impactful resumes and cover letters as well as navigate competitive job markets. He holds an MA in History from the University of Nevada and leads a 10-person in-house team of career professionals and PR experts.
	Midori Chen, SEO Data Analyst Midori Chen is a data analyst with a background in sociological research, holding a Bachelor’s in Gender & Women’s Studies from UC Berkeley. She develops data methodologies behind resume examples pages, including state salary data and top keywords from job postings.

Our original surveys and publications

Most of our headline research comes from surveys we design and field ourselves. Across these studies we use a consistent approach:

Fielding — surveys are run through Pollfish using Random Device Engagement (RDE) sampling to reach a broad, demographically varied group of respondents. Respondents are screened so that only people relevant to each study (for example, active job seekers, or people who hold hiring responsibilities) are included.
Sample — studies are typically based on around 1,000 U.S. respondents, with demographic breakdowns by generation, gender, education, and region.
A note on sampling — these are opt-in online surveys, not probability-based samples. They are well suited to identifying broad patterns and differences between groups, but they do not carry the same statistical guarantees as a probability sample, and results should be read as indicative rather than as precise population estimates.
Privacy — before we publish raw data, we de-identify it — geography is reported no finer than state, ages are grouped into generations, income is reported in broad bands, and any demographic group with fewer than five respondents is suppressed in our published tables.
Open data — the raw, de-identified datasets behind these surveys are published openly on GitHub under a Creative Commons Attribution 4.0 license, free to reuse with credit: github.com/Resume-Genius-Official/research-data.

Our original surveys, as well as reports and articles based on survey data, include:

2026 US job seekers survey

Original survey data can be found on Github.

2026 US hiring managers survey (Q1)

2026 Hiring Insights Report

Original survey data: Github

2025 US full time workers survey

Original survey data: Github

2025 US Gen Z workers survey

Original survey data: Github

2025 US hiring changes survey

Original survey data: Github

2024 US post-election salary and romance survey

Original survey data: Github

2024 US job seekers survey

2024 Job Seekers Insights Report

Original survey data: Github

2024 US hiring trends survey

Original survey data: Github

2023 US workplace skills survey

2023 Workplace Skills Report

Original survey data: Github

2022 US cover letters significance survey

Cover Letter Statistics

Original survey data: Github

Research based on external data sources

Some of our research analyzes data published by government agencies, research institutions, and industry sources rather than data we collect ourselves. A few studies combine external data with our own survey findings. In every case we cite the original source directly so readers can trace any figure back to its origin, and we note the release year of the data we used, since these datasets are revised over time.

Our primary public sources are from the U.S. Bureau of Labor Statistics:

Occupational Employment and Wage Statistics (OEWS) — wage estimates by occupation, including state-level figures.
Occupational Outlook Handbook — occupational descriptions, entry requirements, and outlook.
Employment Projections — ten-year projected employment growth by occupation.
Current Population Survey (CPS) — workforce composition, including the share of workers by gender and age group.
Injuries, Illnesses, and Fatalities (IIF) — fatal and non-fatal workplace injury rates by occupation.

We also draw on other public and non-profit sources:

O*NET Online — occupational characteristics such as stress tolerance, physical demands, social interaction, and hazard exposure.
U.S. Census Bureau, American Community Survey — demographic and workforce context.
Peer-reviewed and institutional research, including Frey and Osborne’s 2024 reappraisal, “Generative AI and the Future of Work” (Brown Journal of World Affairs), which informs our assessments of AI resilience.
Published findings from organizations such as Pew Research Center and Mental Health America.

Where a study requires data that no public dataset provides, we use industry and commercial sources, and identify them in the study’s methodology:

Burning Glass Institute Credential Value Index and Skillsoft’s IT Skills and Salary Report — credential wage outcomes.
LinkedIn and Lightcast — workforce, skills, and alumni employment data.
Job board listings data, including RemoteJobs.io and ZipRecruiter, for advertised salaries where survey wage data isn’t available.
LMI Institute automation exposure scores — occupational automation risk.

Studies based on public datasets include:

Methodology: average salaries on resume examples pages

Many of our resume examples pages show a typical salary for the relevant occupation. These figures come from the U.S. Bureau of Labor Statistics’ Occupational Employment and Wage Statistics (OEWS) program, which publishes annual wage estimates by occupation and by state.

For each resume example, we match the occupation to its corresponding BLS occupation code and report the mean annual wage. Where we show salaries by state, those figures are drawn from OEWS state-level estimates. Because BLS updates OEWS once a year, we refresh these figures when a new release becomes available; each page reflects the release year shown on the page.

Methodology: top keywords in job ads

Some of our pages highlight the skills and terms that appear most often in real job postings. We compile these by aggregating a large sample of publicly posted job ads and analyzing the language they use.

To turn raw posting text into usable categories, we use a large language model for a specific, bounded task: extracting skill and keyword phrases from posting text and grouping near-duplicates (for example, treating “MS Excel” and “Microsoft Excel” as the same skill). The model is used to normalize and cluster language, not to generate findings on its own. Aggregated results are reviewed by our team before publication.

We report keywords as aggregate frequencies across many postings; we do not publish or retain individual job ads, and figures describe broad patterns rather than any single employer or listing.

How to cite our research

If you reference our research or data, please credit Resume Genius with a link to the original report. For example:

Resume Genius. “[Report title].” [Year]. [report URL]

Raw datasets for our original surveys may be reused under CC BY 4.0 with attribution; see the data repository for citation details.

5 Years of Experience

22 Articles Written

Jack Hulatt graduated from the University of Greenwich with a bachelor’s degree in Politics and International Relations before pursuing certifications in computer science and data analytics. In his short career, he has already worked on multiple entrepreneurial projects and as part of a larger team, giving him direct insight into the needs and wants of young job seekers today.

Expertise

Data Analytics International Relations Video Production Python Creative Writing Digital Marketing