[EMA] Analytics 101

Companies have been using statistical models, machine learning techniques, and basic data analytics to forecast demand, drive growth, improve efficiency, etc, for decades. What has made the last 15 years different from previous decades are the massive explosion in the types and amount of available data, advances in data, computing, and analytics technologies, and improvement in statistical models and machine learning techniques. These improvements and innovations have driven a wide and rapid adoption of "data science" across a wide range of industries in the last 15 years or so. In fact, data science and analytics are ubiquitous in the entertainment industry nowadays. Entertainment companies use data science and analytics to produce data-driven insights to produce better content, products, and services, improve audience experience, increase engagement, reduce churn, optimize marketing spends, etc. Designed as a high-level overview, in this 60-minute presentation I will (1) provide an overview of a range of popular statistical models, machine learning techniques, and data analytics methods with several use cases from the music industry; (2) introduce common concepts used in analytics; and (3) offer some personal opinions on how data science and analytics should be applied to solve business problems in the entertainment industry.


Speaker background:

Jeffrey is currently the Global Head of Data Science and Analytics at Amazon Music. He leads and grows a global team of data scientists, data engineers, machine learning engineers, and business intelligence engineers to develop both insights-packed analytics and end-to-end statistical and machine learning systems that spread across a wide range of data-driven initiatives within Amazon Music. Prior to Amazon, Jeffrey worked at WalmartLabs, now known as Walmart Global Tech, as the VP of Data Science, Data Engineering, and Platform Engineering.

Before joining WalmartLabs, I pretty much spent my entire career in quantitative finance. My last role in the investment management industry was the Chief Data Scientist and Global Head of Data Science at AllianceBernstein (AB), a global investment management firm that managed over $550B by 2018. At AB, I led all data science and data engineering initiatives, ranging from equity, fixed income, multi-asset investments, retail client sales and marketing, institutional client growth, and sell-side investment research. Before AB, I was the VP and Head of Data Science at Silicon Valley Data Science (SVDS), a startup acquired by Apple in 2017. At SVDS, I led a team of Ph.D. computer scientists, statisticians, and other scientists helping global companies transform their businesses using advanced data science techniques and modern data technology.

Earlier in my career, I held various quantitative leadership positions, including the corporate VP and Head of Risk Analytics and Quantitative Research at Charles Schwab Corporation, Director of Financial Risk Consulting at KPMG, and Assistant Director at Moody's Analytics.

I enjoy academic research and teaching. In fact, I started my career as a tenure-track Assistant Professor of Economics at Virginia Tech, and I was an adjunct professor at UC Berkeley, Cornell, and NYU, teaching machine learning for finance, machine learning for business analytics, advanced statistics for data science, and analytics for Chief Data & Analytics Officers. I am a frequent speaker at national and international A.I., data science, and technology conferences, such as Spark&AI Summit, Strata, ODSC, PyCon, and many others. By training, I am an Economist, with a strong focus in econometrics. I hold a Ph.D. and an M.A. in Economics from the University of Pennsylvania and a B.S. in Mathematics and Economics from UCLA.

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