Virtual Career Fair - Insurance & Finance Companies

About the Speakers

Nathan Lally is an AVP of data science at Hartford Steam Boiler (Munich Re Group) where he leads a team working on underwriting, pricing, IoT and other modeling projects. Prior to working at HSB, he worked in engineering statistics at Pratt and Whitney, as a data scientist at The Hartford Insurance Group and also has taught ‘Introduction to Biostatistics for Health Professionals’ as an adjunct instructor at the University of Connecticut. In his spare time, Nathan works on actuarial research topics, presenting papers at the annual Actuarial Research Conference on predictive modeling in long-term care insurance, spatiotemporal modeling of damages to electrical infrastructure, and Bayesian non-parametric methods for loss reserving. Nathan holds undergraduate degrees in Political Science and Mathematics/Statistics from the University of Connecticut as well as an MS in Mathematics.

Qingqing (Anna) Dai is a Data Scientist currently working at Liberty Mutual Insurance.  She works in the Boston office and has been with Liberty for two years.  Qingqing specializes in solving problems using both traditional statistical methods and machine learning algorithms. At Liberty, she focuses on building and deploying predictive models to support the company’s commercial lines claims and actuarial teams. She has provided comprehensive data analyses and built several machine learning models to improve the efficiency of the claims handling process.  Qingqing is passionate about leveraging data science to reduce the organization’s costs and to optimize the claims experience for Liberty’s customers.  In addition, she is interested in applying natural language processing techniques and deep learning algorithms to solve business problems.  Qingqing holds a PhD in Statistics.

Daniel (Dan) McCarthy, is a Sr. Campus Recruiter at Liberty Mutual Insurance. He is aligned to our graduate internship program team where he recruits both Masters and PhD students. Dan has been with Liberty Mutual since 2019. 

Siddhartha (Sid) Dalal has made numerous contributions in the field of Data Sciences and Services with over 100 co-authored publications including four monographs and several patents covering the areas of data mining and machine learning, statistics, risk analytics, econometrics, software and network engineering. He is a recipient of four best-paper awards by well-known professional societies. Sid also coined the phrase Statistical Software Engineering and was a coauthor of the NRC report [87] of the same name published by the National Academy of Sciences.

Sid is a Professor of Professional Practice at Columbia University. Prior to Columbia he was Chief Data Scientist and Sr. VP at AIG in charge of R&D that included creation and application of AI, Statistics and CS to Computer Vision, Natural Language Processing and Sensors/IOT for managing risks. He came to AIG from RAND Corporation where was the CTO. Sid also was VP of Research at Xerox overseeing their worldwide imaging and software services research, and Bell Labs and Bellcore/SAIC as Chief Scientist and Executive Director.

Sid has an MBA and a Ph.D. from the University of Rochester with over 100 peer reviewed publications, patents and monographs covering the areas of risk analysis, medical informatics Bayesian statistics and economics, image processing and sensor networks. At Rand he was responsible for the creation of technology and spinning off of Praedicat, Inc., a casualty insurance analytics company. Sid is a member of US Army Science Board, an advisory board of 20 scientists appointed by Secretary of Defense to advise US Army on technology. He has received several awards including from IEEE, ASA and ASQ.

About the Moderator

Susan Edwards is a research statistician at RTI International in the Division for Statistical and Data Sciences. At RTI, her work involves data processing and management, statistical data analysis, sample selection, quality control, imputation for missing data, sample weighting, and statistical programming. Susan’s statistical interests include missing data, causal inference, categorical data analysis, supervised and unsupervised learning methods and correlated data analysis with an interest in applying these techniques to complex survey data. Susan is currently working toward a PhD in Biostatistics from the University of North Carolina at Chapel Hill.