Abstract: Data scientists need to work on almost every aspect of a business. If a team is built only by "pattern matching” current (or idealized) members, then it will only be great at solving well-understood existing problems—and it will likely flounder as the business makes increasingly complex asks. This creates the pernicious conclusion that it’s tough to get a return on data science investments.
Some of the most important benefits that data science teams can deliver are creative and novel solutions. The key to success when exploring uncharted territory is to have a diverse team: one with a wide-ranging background of experiences, demographics, areas of specialization, computer languages (and natural languages too!), diplomas (don’t hire only PhDs!), favorite approaches and algorithms, etc. To build strong data science teams (and to set them up for long-term success), it is crucial to optimize their makeup across many dimensions and to instill in them the importance of caring deeply about data assets (from data collection and security, to ethics and interpretability).
This will be a useful session for those already working in industry who want to get better ROI from data science, teachers who want to get a better understanding of what hiring managers look for, students seeking out what to learn in order to be better positioned for data science work, and for data scientists to find out what to advocate for when hiring and how to improve their teams' effectiveness.
Bio: Angela Bassa is the Director of Data Science at iRobot, where she leads the newly-formed team through development of machine learning algorithms, sentiment analysis, and anomaly detection processes. Angela is also a technical advisor for Mirah, a startup focused on making behavioral healthcare more objective and data-driven. Her previous projects earned accolades such as INFORMS’ Edelman award for Achievement in Operations Research and the Management Sciences; and the Massachusetts Innovation & Technology Exchange award for Big Data and Analytics Innovations. She discovered data science while studying math at MIT, only back then it wasn't called that yet. Over the past two decades she has learned to lead data teams in academic, commercial, and industrial applications. Angela also has three patented inventions, as well as 24 patent applications currently pending in the US, the EU, and Australia.