Jeff Heaton, Ph.D., Vice President, leads a multi-level team of ~8 data scientists and data science engineers. Responsible for the selection, implementation, and maintenance of Domino Datalab for over 100 users. Deployment, maintenance, and support of multiple commercial predictive models deployed externally to RGA customers. Data Science, leads a team of data scientists and data science engineers at RGA; he is responsible for selecting, implementing, and maintaining Domino Datalab for over 100 users. He is responsible for deploying, maintaining, and supporting multiple commercial predictive models deployed externally to RGA customers. He has experience with Python, R, XGBoost, TensorFlow, PyTorch, and Spark. Jeff works directly with business leaders across multiple divisions to bring models into production usage. Over 20 years of insurance experience in underwriting, retrocession, and premium calculation.

As lead data scientist in RGA Reinsurance Company’s Global Research and Data Analytics Group, Jeff Heaton works primarily with modeling risk for underwriting systems using electronic health records. In this role, Jeff’s IT background allows him to bridge the gap between complex data science problems and proven software development.Jeff joined RGA in 2001 as a member of the Information Technology (IT) group to design and implement systems for retrocession administration and premium calculation, and transitioned to a data science role in 2013. He is a frequent speaker and author for organizations such as the Society of Actuaries (SOA), the Institute of Electrical and Electronics Engineers (IEEE), and for academic journals. Jeff has authored several books on artificial intelligence, and teaches a graduate course at Washington University on this subject. Jeff is a senior member of the IEEE and a Fellow of the Life Management Institute (FLMI). He holds a Master of Information Management (MIM) degree from Washington University and a Ph.D. in computer science from Nova Southeastern University.

Senior data scientist in the Global Research & Development group at RGA. Using R, Python and SQL I create models for a variety of different business needs including underwriting and actuarial.

Data scientist, working in the underwriting division of RGA, working with our company's Electronic Health Records (EHR) initiatives. I made use of machine learning and predictive modeling to process continuation of care (CCD) documents for underwriting purposes. I am familiar with EMR standards such as ICD10, SNOMED, LOINC, CPT, HL7 and RxNorm. I make use of technologies such as R, Python Scikit-Learn, and C#.

A variety of software engineer roles ultimately culminating in Lead Systems Analyst. Designed and implemented a wide array insurance modules for premium calculation, claims processing, retrocession, underwriting, accounting and reporting. Used technologies such as Java, Groovy, SQL, Jenkins, Gradle, Maven and Toad. Responsible for implementing batch processing using threading and clustering on large data sets. Created many large and complex reports from large data sets.

My course is T81-558: Application of Deep Learning is a technical graduate course that covers deep learning using Python, Scikit-Learn, Pandas, Keras, and TensorFlow. I also occasionally talks/seminars at Washington University. You can read more at my website: http://sites.wustl.edu/jeffheatonDeep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks of much greater complexity. Deep learning allows a neural network to learn hierarchies of information in a way that is like the function of the human brain. This course will introduce the student to computer vision with Convolution Neural Networks (CNN), time series analysis with Long Short-Term Memory (LSTM), classic neural network structures and application to computer security. High Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well as grids. Focus is primarily upon the application of deep learning to problems, with some introduction mathematical foundations. Students will use the Python programming language to implement deep learning using Google TensorFlow and Keras. It is not necessary to know Python prior to this course; however, familiarity of at least one programming language is assumed. This course will be delivered in a hybrid format that includes both classroom and online instruction.

Taught classes on SQL, VB and C# programming.

Designed and taught courses on Java, C/C++, UNIX Administration, C# and SQL. Created a popular Java certification course.

Wrote Java and C/C++ software for insurance administration and underwriting. I joined this company when it was known as Genelco, part of General American, later it spun off to Navisys, later to be acquired by Accenture.

Worked as a consultant on several short-term client-server type projects at Boeing, MasterCard & Monsanto. Primary languages were C++ and SQL.

Extended and supported a C++ application that organized factory resources and workers for the most efficient production of required bakery products. During my time here, this company was known as Campbell Taggart (part of Anheuser Busch), spun off to become The Earthgrains Company, and then they later outsourced most of IT (myself included) to EDS.

I worked for two startup companies during the earliest part of my career: CompareWare & ION Systems. Both ultimately went out of business. I primarily did C++/WIN16/32 programming client-server desktop applications that allowed customers to access these systems. This involved clients accessing server computers over dial up modem. CompareWare was a system that allowed request for proposals (RFP) to be posted for print jobs. ION systems was a very early attempt at online news display (over dial up).