Leveraging AI Data for Recruiting: Insights from Beacon

Join Heath Black, Managing Director of Product at Signal Fire, as he shares insights from his company's proprietary AI/ML platform Beacon, and discusses how data can be used to filter, find, and close the right people for your team.

* Heath Black is the Managing Director of Product at Signal Fire.
* Signal Fire is a VC firm that operates like a tech company, using AI and ML tools to support its portfolio companies.
* The firm's proprietary platform, Beacon, tracks over 650 million employees, 80 million companies, and 200 million open-source projects.
* Beacon builds proprietary ranking systems and market insights to help the firm move at startup speed and support its investments.
* Today's focus will be on using data from Beacon to filter, find, nail the timing, and close the right people for opportunities.
* There has been a stark democratization in AI startups hiring engineers without PhDs or prestigious schooling over the past decade.
* In 2015, 27% of engineer hires were from top schools and 16% had PhDs; in 2023, those numbers were 15% and 7%, respectively (a 50% decline).
* For research scientists, about 40% have advanced degrees, but they make up less than half of people in research scientist roles.
* The shift from credentials in AI has also resulted in increased people mobility between companies.
* Historically, AI talent was centered on large tech companies like Google, Uber, Meta, and Apple; now, it's shifting towards the "AIv League" (OpenAI, Cohere, etc.).
* Companies on the left side of the screen are fighting to get people from the right side, indicating a shift in talent concentration.
* When filtering for talent, consider work experience over education; look at open-source contributions and real-world experience.
* In many cases, a PhD researcher may not be necessary for roles where an experienced engineer can suffice.
* Remove or soften academic requirements in job postings to ensure the top of the funnel includes people with the right experience.
* San Francisco remains a significant hub for AI talent, with 27% of AI professionals living there.
* Signal Fire has built a tool called "historical composition" to help startups identify risk profiles and motivations of potential hires by examining companies they admire at different points in time
* To understand timing, track competitors and companies you admire to know when they're likely to lose talent and when potential hires are likely to join a new company.
* Study the patterns of different generations or segments of the population to better understand how they change jobs.
* In the current landscape, pay and equity can no longer be relied upon as sole narrative components for recruiting.
* A close-knit environment, collaborative teams, big mission, career growth opportunities, and markets that are exploding and solving complex problems should also be part of a company's narrative.
* In a competitive recruitment landscape, data can give you an edge in building your team, just as it does for building your product.

Source: AI Engineer via YouTube

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