Em

Employees.fyi – Easily compare U.S. workforce demographic data

Hacker News

Employees.fyi – Easily compare U.S. workforce demographic data

Hi HN! We built Employees.fyi to make it easy to compare U.S. workforce demographic data across companies and against industry reference data. In the U.S., the Equal Employment Opportunity Commission (EEOC) requires the collection and submission of demographic workforce data. We collected and organized the publicly available federal data from the EEOC as well as publicly available EEO-1 submissions from individual companies. By doing so, we hope to make it easy to compare U.S. workforce demographic data across companies and against industry reference data. The URL contains your current selection. Just copy the URL and share it! Some examples: * A comparison of 2018 data for the "Professionals" job category across the Information industry, Facebook, and Netflix: https://employees.fyi/?year=2018&job=PROFESSIONALS&reference... * A comparison of 2018 data for all job categories across the Finance and Insurance industry, BlackRock, and PayPal: https://employees.fyi/?year=2018&job=ALL&reference=52&compan... * A comparison of 2018 data for the "Exec/Sr Officials & Managers" category across the Professional, Scientific, and Technical Services industry, Accenture, and Nvidia: https://employees.fyi/?year=2018&job=SRMANAGERS&reference=54... If there's a company with EEO-1 data that you would like to see, consider submitting a URL via this form: https://forms.gle/8cVfXpg69fiiemzc8 Let us know what feedback you have for us! For those who are curious: at runtime, Employees.fyi uses normalize.css and the Open Sans font. They are hosted with the website.

Share card

Actual performance

124points
79comments
Made the leaderboard

Launch Intel predictions

Analyze your own launch →
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
52%52% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive
31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: open · Missing: mac, agents, macos
28%28% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

Similar products

Co
Compare Knives Easily46%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Compare Knives Easily

Hacker News2
Fi
First Employees40%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

First Employees

Hacker News1
ev
everything for employees40%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

everything for employees

Hacker News3
Statily
Statily16%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

A comprehensive software to monitor employees for accurate workforce analytics

AppSumo3
Tr
Track your employees moods with CompanyMood36%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Track your employees moods with CompanyMood

Hacker News8
Fi
Find out if employees have been in a data breach, leak, or hack44%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Find out if employees have been in a data breach, leak, or hack

Hacker News7
Co
Compare Two Samples61%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Compare Two Samples

Hacker News1
Co
Compare Billionaires47%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Compare Billionaires

Hacker News1
Co
Compare two DOM strings and find minimum difference between the two43%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Compare two DOM strings and find minimum difference between the two

Hacker News4
A
A resource to compare headphones and amps32%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

A resource to compare headphones and amps

Hacker News5