Founder & CEO Laura Gomez demos how Atipica uses data & machine learning to predict hiring needs

TL;DR

Using analytics and data-driven decisions can help companies improve workforce diversity and make more informed hiring choices.

Transcript

we really think about analytics and data-driven decisions around the modern workforce as you see here when we don't talk about hey it feels good to hire a woman is much more like are there more women with this degrees I want to apply your company and make it more diverse in it the diversity of the product we do have I just right before I came here ... Read More

Key Insights

• 🆘 Data analytics can help companies understand and improve workforce diversity.
• 🥳 Men have more job options overall, leading to potential issues with hiring ratios.
• 🧔‍♀️ Women are more likely to get hired when they apply, indicating the company's efforts towards diversity.
• ❓ Statistical significance testing is important in understanding hiring biases and behaviors.
• 📜 Companies should document specific reasons for not hiring applicants to address potential biases.
• 🤽‍♀️ Timing appears to play a role in whether women consider a job opportunity.
• ❓ Bias in interviewers or interview processes can impact hiring decisions.

Questions & Answers

Q: How does the company use analytics to predict applicant information?

The company extracts relevant information from resumes and uses public and industry data alongside machine learning to predict aggregate applicant information.

Q: What is the accuracy rate for predicting an applicant's gender?

The accuracy rate for predicting gender based on resume data is approximately 96%.

Q: Are there any patterns in the likability and hiring rates of male and female applicants?

Women are more likely to get hired when they apply, while men have more job options overall. Men are also less likely to get hired, possibly due to rejecting offers from the company.

Q: How does the company analyze statistical significance in hiring decisions?

The company's statistical significance testing takes ten times longer than their original algorithms, as it examines the likelihood of being hired, applicant behaviors, and reasons for not hiring.

Summary & Key Takeaways

• Companies can use data analytics to extract information from resumes and predict aggregate information about applicants, such as gender.

• The accuracy rate for predicting gender based on resume data is approximately 96%.

• Women are more likely to get hired when they apply, while men have more job options overall.