Dave Levy

Dave Levy

Dave Levy, vice president at Amazon Web Services, leads the company’s U.S. government, nonprofit and healthcare businesses. He and his teams help governments, nongovernmental organizations, nonprofits and healthcare providers realize the potential of technology to transform their organizations and fulfill their missions.

Dave is an experienced executive and impactful leader in the information technology sector and, for more than 20 years, has passionately focused on the intersection of technology and organizational change. Prior to joining AWS, he worked for Apple Inc. for 12 years and led the teams that helped the government adopt innovative mobile technologies. Under his leadership, Apple’s government business grew nearly 300% in a four-year period. As head of the U.S. public sector, he played a key role in the introduction and adoption of mobile apps and app stores in federal, state and local governments. Before joining Apple, Dave worked for Monster.com and helped lead its customer-facing teams, which played a key role in bringing automation and innovation to federal hiring and recruiting platforms.

Additionally, as co-founder and chief operating officer of Sulla Technology Group, he built a successful datacenter services company focused on higher education clients, and state and local governments. Before founding Sulla, Dave started Empire Capital Management to focus on the investment needs of mid-market institutional clients.

Dave currently serves on the research, education and innovation advisory board for Children’s National Medical Center in Washington, D.C. He also serves as the chair of the Procurement and Space Industry Council for the U.S. Chamber of Commerce and is on the board of advisors for Earth Day Network.

His studies include a Bachelor of Science in international economics from Texas Tech University. He and his wife Izumi have twin daughters and currently reside in Arlington, Virginia.

How Nonprofits Are Using Machine Learning To Support Those in Need

Just a decade ago, machine learning was only accessible to the largest, most well-funded companies and research institutions. Opportunities for nonprofits to use it to advance their work were simply out of reach due to high costs and lack of technical expertise.