Abstract: 

Each day, AI and data science become less of a competitive advantage for businesses and more of a requirement for survival. Yet, many companies struggle to effectively leverage their data science and AI investments. New Vantage Partners reports that although 92% of businesses are increasing investment in AI, 77% continue to face challenges with adoption. What makes some companies reap the benefits of AI while others struggle to see return on investment? The difference is strategy.

Whether your teams are distributed or centralized, many or few, sophisticated or just starting, your company needs a unified data strategy. But, data science and AI bring new challenges to strategic planning. The nature of their output demands that data science and AI projects undergo a more risk-aware scoping process. To scale smoothly and evolve quickly, a data strategy needs to provide broad and specific standards. Finally, proposed projects need to be contextualized within the larger data ecosystem in order to amplify their impact.

This talk is designed for business leaders, data science managers, and decision makers that want to ensure the effectiveness of the AI and data science capabilities they are building. Attendees will leave equipped with the tools to:
Critically evaluate pitched projects and select the most strategic ones;
Build an effective, impactful, and high yield data science project portfolio;
Evolve your data science roadmap to quickly adapt to new opportunities.

Bio: 

Kerstin is passionate about bringing data science from the edge of business to the center of it. She has data science experience in all three sectors: for-profit, non-profit, and government. Currently, she is a Senior Data Scientist at Metis where develops and delivers curriculum to accelerate data science learning for teams. As Director of Data Science, she founded the Guidestar data science team and brought machine learning to the largest nonprofit data warehouse. At Postmates she used her broad data science toolkit to support marketing, growth, finance, and fleet team needs. As a University of Chicago Data Science for Social Good Fellow she helped uncover early signals for delays in education. She holds graduate degrees in statistics, mathematical statistics, and mathematical computer science from Cornell University and University of Illinois at Chicago. As an undergraduate she studied psychology and anthropology at Yale University.