Data Science Virtual Panel Discussion
Data science leaders from a variety of organizations talk about the emerging field of data science and what their career paths in data science have looked like. This panel will showcase the wide variety of data science roles, interesting challenges in the data science space, and what employers look for in candidates for different data science positions.
Head of Business Intelligence, Kabam
Kenneth Yu is the head of business intelligence at Kabam, the leader in the western world for free-to-play core games. Kenneth joined Kabam in December 2011 and oversees the analytics and data science teams, focusing on advancing data-driven approaches to improve the performance of key business drivers. Prior to Kabam, Kenneth was a product engineer in the Spansion unit at Advanced Micro Devices Inc., where he developed analytics solutions and innovated on data visualization and performance modeling techniques in the semiconductor R&D domain. He received a B.S. in electrical engineering and computer science from UC Berkeley.
Senior Principal Data Scientist, Cablevision
Khosrow Hassibi is an innovator, practitioner, and thought leader in the use of advanced analytics and machine learning applications in real-world business problems. He has recently joined Cablevision in San Francisco to grow the Data Sciences team with particular focus on advanced analytics use cases leveraging all detailed user and machine data. His expertise is based on twenty years of design, R&D, consulting/sales, and management in applying these technologies to problems such as real-time fraud detection, marketing, risk, OCR, and customer behavior analysis. In particular, he has been recognized for his contributions to real-time payment card fraud detection. He has been a part of four machine learning startups in the past and more recently with SAS Software focusing on big data analytics applications. He has recently authored a book titled “High-Performance Data Mining and Big Data Analytics” which provides a holistic view of big data analytics’ impact across these classic organizations, and also discusses the impact of high-performance computing techniques on legacy data mining.