Abstract: 

Data is in fashion, and rightly so. However, many organizations struggle to “carry” it properly. The promise of data and data analytics is immense, but its actual implementation needs more than just data science PhDs and Hadoop clusters. It requires a mindset shift. What is the right mix of talent to make that happen? What kind of projects need to be undertaken and how to phase them? How to separate the hype of advanced techniques like machine learning from what will work for business in the now and here? Why is scaling important and how does it usually get undermined? As you already have realized while solving this for your organizations, the approach requires a mix of EQ and IQ. While there is no silver bullet, in this session we will discuss how we can be proactively aware of the common pitfalls, and avoid being blindsided by them on our journey.

Bio: 

Delin Shen joined AIG in September 2016 to build a predictive modeling team and drive personal insurance growth and profitability with modern machine learning and analytics. His responsibilities include building and training a predictive modeling team and promoting best practice modeling guidelines, partnering with business partners to identify potential high impact analytics problems and design solutions, and exploring new data sources and tools to improve modeling effectiveness.

Prior to AIG, Delin served as a Senior Director of Statistical Modeling at LexisNexis Risk Solutions, where he was responsible for designing and developing innovative products and analytical solutions for Insurance Data Services, focusing on marketing analytics and telematics.

Delin holds a Ph.D. from the Harvard-MIT Division of Health Sciences and Technology on exploratory data analysis and a B.A. from Tsinghua University with a double major of Biomedical Engineering and Mechanical Engineering.