a woman in shadows
Speakers at the Women in Data Science Conference at Stanford University shared some great career advice. PHOTO: Molly Belle

Women earn more than 50 percent of the bachelor's degrees issued in the US, but still trail men when it comes to degrees granted in Science, Technology, Engineering and Mathematics (STEM) fields.

It's a sobering reality that underscores the importance of events like the second annual Women in Data Science (WiDS) Conference at Stanford University this month.

The one-day technical conference, held by Stanford's Institute for Computational and Mathematical Engineering (ICME), featured 16 women from government, academia, industry and nonprofit sectors, who discussed a range of topics in data science, including the discipline’s role in artificial intelligence, national security and health care.

The event brought together women from 114 companies and 31 universities, with thousands more joining online, to discuss opportunities and challenges in data science — a field typically dominated by men.

As ICME Director Margot Gerritsen noted, “There is this sense among women – more than men – that you need this innate ability to do data science,” Gerritsen said. “We need to change that attitude.”

The goal was to inspire and connect female scientists and engineers — a goal apparently reached after the first WIDS conference in 2015, according to this tweet:

Influential Women

This year the conference drew some of the most successful women in the data business as speakers, including Gerritsen; Finale Doshi-Velez, Assistant Professor of Computer Science, Harvard University; Deborah Frincke, Director of Research, National Security Agency; Yael Garten, Director of Data Science, LinkedIn; Janet George, Fellow and Chief Data Officer, Western Digital; Stephanie Gottlib-Zeh, President, Agyleo Sport;

Diane Greene, founder of VMware and Senior Vice President, Google; Susan Holmes, Professor of Statistics, Stanford University; Sinead Kaiya, COO, Products & Innovation, SAP; Fei-Fei Li, Chief Scientist of AI/ML,Google Cloud, and Professor of Computer Science and Director of Artificial Intelligence Lab, Stanford University; Miriah Meyer, Assistant Professor, School of Computing, University of Utah; Claudia Perlich, Chief Scientist, Dstillery;

Megan Price, Executive Director, Human Rights Data Analysis Group; Lori Sherer, Partner, Bain & Company; Caitlin Smallwood, VP of Science and Algorithms, Netflix, Belle Wei, Carolyn Guidry Chair in Engineering Education and Innovative Learning, San Jose State University; Julie Yoo, Founder and Chief Data Scientist, pymetrics; and Janine Zacharia, Carlos Kelly McClatchy Visiting Lecturer, Stanford University.

The speakers shared the diverse paths they took to get to the top of their fields. While STEM degrees open many doors, they are not a must. Kaiya, for instance, has a degree in Fine Arts. And early in her career Greene, quit an engineering job in the oil industry (because they wouldn't allow women on oil rigs) to wind surf in Hawaii.

This graph shows the fraction of US bachelor's degrees awarded to women in Science, Technology, Engineering and Mathematics (STEM) fields.
This graph shows the fraction of US bachelor's degrees awarded to women in Science, Technology, Engineering and Mathematics (STEM) fields.PHOTO: APS/Source: IPEDS Completion Survey

Career Advice Worth Taking

Collectively the women offered invaluable career advice which might, quite frankly, be helpful to everyone, regardless of job or gender.

If you don't think you have anything to offer, think again

Google (now Alphabet) courted Greene for more than six years. After relentless pursuit, she gave in and joined the board, but kept refusing offers to join the company. When Urs Hölzle, senior vice president technical infrastructure at Google asked her why she didn't want to work at the company, she replied "I really don’t think I’d be successful at Google. I don’t feel comfortable.” His response? “Only a woman would say that, a man would never say that," she told the WiDS audience.

Take Risks

Greene recommended against being as conservative as she has been in her own career. Check yourself when you hold back. If you can tolerate the worst thing that could happen, go for it.

Optimize for Your Interests

Optimize for what you are interested in. So if you are in machine leaning, for example, and your goal is money, do machine learning for a financial services firm. If you are more interested in saving the world, look into machine learning for health. "Health has so much low-hanging fruit," Greene said.

Focus on Your Strengths

Smallwood admitted she sometimes focused more on her weaknesses than strengths. Don't, she advised. Looking at "your strengths is more efficient," she said.

Make Your Voice Heard

"We're at an important tipping point," Kaiya said — a time in our technological development when we need diverse voices to advance society. "The choices we make now will shape the future of the planet, and that's why we need all of you," she said.

Persevere

For data workers, the most important skill is the ability to ask critical questions, said George. Never give up. "You have to stick with it even when the data is not giving you all the answers. When that happens you just have to dig deep."

And if you meet sexism on the job, just keep pushing on. "We are not going away. We just have to get the word out and advocate for ourselves," George said.