Leading Data Science Teams and Managing Career Paths for Analytics Professionals

Tuesday, April 24, 2018 - 2:15 pm

It is 2018. Professionals in the field of data science expect a lot from their leaders and organizations, and they are in higher demand than ever. How do you build a leading edge, modern advanced analytics organization that data scientists are proud to be a part of, and leverages the network effects of the analytics community as a whole? How do you prioritize work that drives organizational goals forward, while at the same time satisfying the insatiable hunger for technical knowledge and “interesting work?” How do you get the most out of cross-functional skills that your team possesses (both technical and otherwise)? How do you ensure that the critical analytics roles in your organization are properly recognized and rewarded for their contributions? How do you generate career paths for data scientists that encompasses experience, exposure, and continued education? Kristen Burton, director of Cisco’s Enterprise Data Science Office will discuss these topics and more.

tina owenmark
Senior Data Scientist
Cisco Systems

Tina Owenmark is a senior-level data scientist with Cisco’s Enterprise Data Science Office, which provides technical leadership for high-impact data science projects as well as various services to support the company’s data science community. She has been at Cisco over 15 years, successfully transitioning to data science from program management about 4 years ago. She was the first woman to receive the Data Scientist title at Cisco, when the company developed its data science career path, and was Cisco’s first employee certified as a Cisco Senior Practitioner of Data Science.

As a data scientist, some of her favorite projects have included supply chain logistics volume predictions, technical support resource allocation optimization, pricing simulation, and expense report fraud prevention. Tina delights in delivering practical solutions that focus on improving business decisions. As she continues her journey, she sets aside time each week for mentoring and building skills. She believes that the strength of her team can be traced in part to a diversity in experience. To that end, she gladly invests in mentoring people who are embarking on data science careers coming from other parts of the business.