12 Inspiring Women In Data Science, Big Data
It’s been well documented that women don’t come close to parity in STEM fields with their counterparts. Could the rise of big data and data science offer women a clearer path to success in technology? Here’s a list of 12 inspiring women who work in big data and data
The lack of women in STEM fields – science, technology, engineering, and math – fields is well documented, and those figures can be discouraging. For instance, women made up 27% of people employed in computer and mathematical occupations in 1960. But instead of growing over several decades, as many more women participated in the workforce overall, that number had declined to 26% by 2013, according to a 2015 analysis of US Census data.
There are a number of successful, prominent women who aspiring data scientists can look to for inspiration. Here’s a collection of a few who spoke at the Strata + Hadoop Women in Big Data lunch and at Stanford’s Women in Data Science conference in November.
- Karen Matthys, Executive Director for external partners at the Stanford Institute for Computational and Mathematical Engineering. She is promoting the next event, the fellowships, and working on the 30by30 campaign, which has a goal of increasing to 30% the women in computer science and engineering roles at all levels of organizations by the year 2030.
- Jill Dyche, VP SAS Best Practices at SAS Institute. She’s the author of several books, including the most recent one, The New IT: How Technology Leaders Are Enabling Business Strategy in the Digital Age. Dyche is currently working on a side project, an e-book that advocates for simple improvements to animal shelter practices to improve pet adoption rates.
- Yanbing Li, VP and GM of Storage and Availability at VMware. Though not strictly a big data pro, Yanbing Li spoke during the Women in Big Data about focus and career goals — and about how she made adjustments to the roles she pursued within VMware to put her on a path to her larger strategic goal of becoming the CEO of a large company. “We need to be very conscious of not always resorting to the path of least resistance,” she told attendees at the lunch.
- Jana Eggers, CEO at Nara Logics. She is CEO of Nara Logics, an artificial intelligence company that is leveraging new neuroscience discoveries to model data on computers. She’s previously owned and operated a number of other companies. She has served as a director of the Innovation Lab at Intuit and as a GM of QuickBase there, and has also worked as an analyst.
- Megan Price, Executive Director at Human Rights Data Analysis Group. Her organization leverages statistical analysis to surface evidence for use in testimony to push for action and change. The group has worked on a number of projects in locations that include Guatemala, Columbia, and Syria. For the Syria projects, Price served as lead statistician and author of two recent reports commissioned by the Office of the UN High Commissioner of Human Rights on documented deaths in that country. She is a research fellow at the Carnegie Mellon University Center for Human Rights Science, and earned her PhD in biostatistics.
- Neha Narkhede, Cofounder and CTO at Confluent. Narkhede is one of the cofounders of Confluent, a company driving a popular big data tool that enables real-time streaming capabilities – Apache Kafka. Narkhede and her cofounders originally developed the technology when they were all working at LinkedIn.
- Amy O’Connor, Big Data Evangelist at Cloudera. She joined Hadoop distributor Cloudera in 2013, coming to the company from Nokia, where she served as senior director of big data. In her role at Cloudera, she advises customers as they introduce and adopt big data solutions. She holds a BS in Electrical Engineering from the University of Connecticut and an MBA from Northeastern University.
- Monica Rogati, Equity Partner, Data Collective at Advisor, Insight Data Science. Monica is the former VP of Data at wearables company Jawbone and a former data scientist from LinkedIn. Today she is focused on providing technical due diligence and advice to the Data Collective venture capital group and serving as an advisor for the Insight Data Science Fellows Program, a post-doctoral training fellowship for bridging the gap between academia and data science careers.
- Jennifer Tour Chayes Distinguished Scientist and Managing Director at Microsoft Research. She presented at the first Women in Data Science conference at Stanford in November 2015. Chayes said during the career panel discussion at the Women in Big Data event at Stamford in November. “You shouldn’t let your fear about your own abilities or a fear that you might be an impostor or something have any bearing on the kinds of decisions that you make. You should just take that part of your brain and say thank you for sharing and just put it aside. We all have that part of our brain and if I’d listened to that part of my brain I would have had a very boring life.” Chayes holds a PhD in Mathematical Physics from Princeton University.
- Caitlin Smallwood, VP, Science and Algorithms at Netflix. She leads an advanced group of mathematicians, data scientists and statisticians at this digital entertainment company. Her group focuses on predictive modeling, algorithm research and prototyping, and other deep analytics across the company. Her career has included work at Yahoo as the director of data solutions and at PricewaterhouseCoopers as a senior manager in quantitative consulting.
- Carrie Grimes, Distinguished Engineer at Google. Grimes has spent her career at Google, where she currently works on data-driven resource planning, cost analysis, and distributed cluster management software as part of the Technical Institute Group. Grimes holds a PhD in Statistics from Stanford University and an AB in Anthropology from Harvard University.
- Kelly Thompson, SVP, Global Category Development and Merchandise Solutions at Wal-Mart eCommerce. Thompson directs strategy, structure, and the operating model for Wal-Mart to combine merchandising with data and analytics. Wal-Mart is one of the biggest companies in the world, and people think of big companies as being slow, but Thompson said her organization is actually building something more agile inside this big company.
Based on InformationWeek slideshow.
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