Leen Schafer is a Data Scientist at Viasat Inc. Leen has developed data science at Inova Diagnostics, from integrating data-driven approaches for internal processes to nurturing the development of the first machine learning-based models. A bilingual Systems Biologist and Data Scientist, Leen graduated with her Ph.D. in Bioinformatics and Systems Biology from UCSD where she focused on utilizing machine learning algorithms to analyze and understand single-cell RNAseq data.
Many companies are catching the buzz and sex appeal of the data science hype that has rapidly developed over the last decade. In the medical diagnostics field, machine learning and multiple data-point-driven decisions are touted as the next big breakthrough in Precision Medicine. Although the medical field generally recognizes the appeal of machine learning and AI, the terms evoke irrational expectations about the accuracy of such models and/or a rush to implement such models prior to developing a clear understanding of successful data curation for data science, the multivariate nature of data science, how to use data science tools, and the requirement for the development of underlying robust analytics within the preexisting systems engineered in the company. Here, I talk about the lessons learned from starting data science in such a company from the ground up, building a culture of collaboration, and showing value to drive data-based investments. This talk will help you gain insights on how to fine-tune and harmonize expectations for both company leadership and data science teams.