We’ve all heard that rare breed the data scientist described as a unicorn. In building your DS team, should you hold out for that unicorn or create groups of specialists who can work together? Should you centralize or decentralize a team? Michael Dauber, Yael Garten, Monica Rogati, and Daniel Tunkelang discuss the pros and cons of various team models to help you decide what works best for your particular situation and organization.
Michael Dauber is a general partner at Amplify Partners. Previously, Mike spent over six years at Battery Ventures, where he led early-stage enterprise investments on the West Coast, including Battery’s investment in a stealth security company that is also in Amplify’s portfolio. Mike has served on the boards of a number of companies, including Continuuity, Duetto, Interana, and Platfora. Mike’s investments include Splunk and RelateIQ, which was recently acquired by Salesforce. Mike began his career as a hardware engineer at a startup and held product, business development, and sales roles at Altera and Xilinx. Mike is a frequent speaker at conferences and is on the advisory board of both the O’Reilly Strata Conference and SXSW. He was named to Forbes magazine’s 2015 Midas Brink List. Mike holds a BS in electrical engineering from the University of Michigan in Ann Arbor and an MBA from the University of Pennsylvania’s Wharton School.
Yael Garten is director of data science at LinkedIn, where she leads a team that focuses on understanding and increasing growth and engagement of LinkedIn’s 400 million members across mobile and desktop consumer products. Yael is an expert at converting data into actionable product and business insights that impact strategy. Her team partners with product, engineering, design, and marketing to optimize the LinkedIn user experience, creating powerful data-driven products to help LinkedIn’s members be productive and successful. Yael champions data quality at LinkedIn; she has devised organizational best practices for data quality and developed internal data tools to democratize data within the company. Yael also advises companies on informatics methodologies to transform high-throughput data into insights and is a frequent conference speaker. She holds a PhD in biomedical informatics from the Stanford University School of Medicine, where her research focused on information extraction via natural language processing to understand how human genetic variations impact drug response, and an MSc from the Weizmann Institute of Science in Israel.
Monica Rogati is an independent data science executive and advisor who has built key data products and teams at Jawbone and LinkedIn; she now helps startups make the most out of their data. As the VP of data at Jawbone, Monica built Jawbone’s data science and engineering team, focusing on developing data products that helped millions lead healthier lives. Her team also analyzed Jawbone’s wearable data to derive novel insights about sleep, movement, and food, then turned these insights into smart product features, compelling data stories, and interactive visualizations. At LinkedIn, Monica was one of the early members of the data science team. She developed LinkedIn’s key data products for job matching and recommendations, and she doubled the effectiveness of the “people you may know” machine-learning algorithm that drives the growth of LinkedIn’s connection graph. Monica is also an equity partner at Data Collective, an early-stage VC firm focused on the big data space.
Monica’s data stories have been published in the Wall Street Journal, the New York Times, and Time and on NPR and CNN. Fast Company recognized her as one of the 100 most creative people in business, and Fortune named her as one of the Big Data All-Stars. She has published numerous academic papers in top-tier journals and conferences and is frequently invited to keynote industry and academic conferences. She has a PhD in computer science from Carnegie Mellon, where she focused on text mining and machine learning, but is now focused on applied data science.
Daniel Tunkelang is a data science and engineering executive who has built and led some of the strongest teams in the software industry. He was a founding employee and chief scientist of Endeca, a search pioneer that Oracle acquired for $1.1B. He led a local search team at Google. He was a director of data science and engineering at LinkedIn, and he established their query understanding team. Daniel also advises and consults for companies that can benefit strategically from his expertise. His clients range from early-stage startups to “unicorn” technology companies like Etsy and Flipkart. He helps companies make decisions around algorithms, technology, product strategy, hiring, and organizational structure.
Daniel is a widely recognized writer and speaker. He is frequently invited to speak at academic and industry conferences, particularly in the areas of information retrieval, web science, and data science. He has written the definitive textbook on faceted search (now a standard for ecommerce sites), established an annual symposium on human-computer interaction and information retrieval, and authored 24 US patents. His social media posts have attracted over a million page views. Daniel studied computer science and math at MIT and has a PhD in computer science from CMU.
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