AI at an Early Stage Startup by Zara Tam

Zara Tam loves building things that make an impact. Now at Verve, she was formerly a product manager at Founders Factory, where she led a revolutionary AI recruitment product called Chosen. Built with the latest machine learning and search technologies, Chosen can help recruiters to find brilliant talent in a matter of minutes.

In this ProductTank London talk, Zara shows how AI can transform the time-consuming processes that companies face when they scout for talent. Zara’s product provides an AI-powered service with a enormous database of candidates at the touch of a button. Zara explains that if a recruiter finds a promising profile on LinkedIn, they can “copy the profile URL and put it into Chosen”. The inbuilt AI will return a comprehensive list of similar candidates based on information from that profile, and the legwork of the search is complete.

How Does it Work?

Chosen requires an enormous amount of data to work, so Zara and her team gathered 400 million candidates from nine million companies. The language-processing AI software extracts relevant information from this database, then sorts the candidates based on the following four categories:

1) The Role Title

The AI can sort the list of candidates into senior or generic-level piles.

2) Function

Chosen analyses the profiles by industry and by speciality.

3) Size

The software can sort profiles based on the size of company they work for. It separates people from big and small businesses, and can identify a start-up or scale-up background.

4) Time

Chosen analyses how much time the candidates have spent in each role, be it a general role, a product management position, or a senior management role. If recruiters would like to hire an experienced member, they can see the time in industry straight away.

What’s Next?

As Zara and her team refine their AI, she continues to share their learnings with product people. She says:

  1. Sprinting doesn’t work! Trial and error and research takes time.
  2. You can find ways to put your data scientists in the shoes of your users.
  3. As a product manager, try to understand how AI works, not just why.

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