Ya Xu | Using Data To Create Economic Opportunities For All Members Of Global Workforce episode artwork

EPISODE · Apr 15, 2020 · 40 MIN

Ya Xu | Using Data To Create Economic Opportunities For All Members Of Global Workforce

from Women in Data Science Worldwide · host Professor Marot Gerritsen

Ya Xu manages LinkedIn’s global team of data scientists that manage data science projects across the company’s products, sales, marketing, economics, infrastructure, and operations. She says the company takes active responsibility over the data they collect to ensure fairness and protect privacy. They are very proactive about how they maintain their members’ trust, either with how they share the data externally or leverage the data to create opportunities.LinkedIn’s fairness mission is that two people with equal talent should have an equal shot at opportunities. To reinforce this, they constantly test new products to determine if a new feature introduces any unintended consequences that might impact fairness. For example, LinkedIn’s referral button allows a job applicant to see if someone they know works in the company and ask for a referral. The unintended consequence is this feature will benefit individuals who have a big network vs. the general population. She says they typically have about 500-600 experiments running concurrently to test new products.One feature that encourages fairness is the push notification that anyone can sign up for that alerts you for when a new job becomes available. That notification is not dependent on your network, and they’ve noticed that this feature especially benefits people who don't have a strong social capital. They share examples like this to help product managers build more socially responsible features.The company has also found that when networks become more diverse, it increases mobility in the labor market. For example, LinkedIn’s Plus One Pledge that encourages people to accept an invitation from somebody who they traditionally would not have connected with has been very successful in opening opportunities for those with less social capital.On the topic of women in the workplace, she encourages women to advocate for themselves. “When I first joined Microsoft, I was the first statistician on the team. People didn't know what to do with me so I just defined what a statistician should be doing,” she says. “It’s up to you to define what kind of role you play in an organization. The more reactive you are, the more that people are going to give you orders. The more proactive you become, the better it is for the company.”Ya has been an individual contributor for most of her career focused on solving a specific problem, but once she became a manager, her perspective shifted. “What excites me now is actually being able to help my team to be more successful. It's almost like there's no way I could solve the problem better than the way that my team can solve the problem. It really excites me when I see their achievement.”She believes that women lead differently. “I have seen women leaders in extremely prominent positions be so humble,” she says. “I think women can be a lot more vulnerable, and it's actually a strength. When we are vulnerable in front of our team, then they relate to us. There's something different about women, and in a very good way.”RELATED LINKSConnect with Ya Xu on  LinkedInRead more about LinkedIn EngineeringConnect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFind out more about Margot on her Stanford ProfileFind out more about Margot on her personal website

Episode metadata supplied by the publisher feed · Published Apr 15, 2020

Embed this episode

Ya Xu, head of LinkedIn’s global data science team, explains how the company takes responsibility for data privacy and creating economic opportunities for all of its members.

Distinct summary based on available episode metadata or transcript content.

NOW PLAYING

Ya Xu | Using Data To Create Economic Opportunities For All Members Of Global Workforce

0:00 40:09

No transcript for this episode yet

We transcribe on demand. Request one and we'll notify you when it's ready — usually under 10 minutes.

No similar episodes found.

No similar podcasts found.

Frequently Asked Questions

How long is this episode of Women in Data Science Worldwide?

This episode is 40 minutes long.

When was this Women in Data Science Worldwide episode published?

This episode was published on April 15, 2020.

Can I download this Women in Data Science Worldwide episode?

Yes. Use the download control on the episode player to save the publisher-provided media file.
URL copied to clipboard!