Analytics (and Big Data) Experience Summary (if such a thing is possible)…

Summary:(Please: ask for any and all detail, this is an extremely concise version of a long career)

Analytics experience covers 20 years, 28 firms and 20+ papers and research initiatives (custom models, novel practices and products, etc.).

Advanced mathematics degree and research.

Member of numerous industry mathematical and statistical expert committees.

Broad experience is all facets of Analytics development, management, and delivery, including:• Model Design, Development and POC (hands-on in R/SPlus, C#, C++, java,

Mathematica, Q/KDB, APL, and other languages; and numerous database, reporting, and visualization tools).

• ETL• Data Management• Data Visualization• Machine Learning• External Data Sourcing• Reporting and Business Intelligence• Platforms for: Discovery/Experimentation, “Big Data” compute and storage, on-

premise and cloud offerings, programming and statistical packages, SQL and NoSQL databases, and ETL/data ingestion tools.

• Opportunity Management• Demand Management• Operating and Communication Models• PMO• SDLC and Delivery• Vendor Management• Architecture, Compliance, Security, Privacy, and COEs.• Liaison and Relationship Management

Recent Work Experience:

Prudential (Newark, NJ): • Leader of Data Analytics Technology Working Group, Line of Business

Architecture, and Relationship and Vendor Management for firm-wide Analytics (predictive, behavioral, machine learning, modeling, “big data”, etc.) “Hub” service creation. Defined process for Analytics initiatives via a process-driven PMO (for procurement, demand management, approvals, vendor management, etc.).

• Defined “horizontal” Managed Services offerings (speech analytics, text mining, clustering, time series).

• Dual-Platform (on-premises and cloud) standup of analytics reference architecture and discovery environment in support of analytics use cases. Oversaw 15 RFI/RFP submissions for Big Data platform development.

• Liaison to Prudential CIO Roundtable and “Leveraging Opportunities” (Enterprise) Executive Management.

• Advisory to all vertical business lines (Annuities, Group, Investments, Retirement, Individual Life, Digital, etc.) and horizontal units (EARB, COE, Privacy, Procurement…) on use of platform, data science, data management, tooling, and managed services within analytics projects.

• Project Manager for Digital Analytics and Predictive Underwriting core implementation teams.

Office of the Chief Medical Examiner (New York, NY) Member of CME Executive Advisory Board.  Reporting accountability to both the

Mayoral Office of New York City and U.S. Department of Homeland Security (for disaster operations).

Analytics and Data Management for: Medico-Legal (crime scene) Investigations, Pathology, Forensic Biology, Decedent Identification, Anthropology, SIDS/SUID Research, Legal, Records, Fulfillment, and Case Management, NYPD Missing Persons / Reported Missing data clustering, Communications, Transportation and Mortuary Operations.

Operational Workflow and Mathematical Model frameworks (including technical architecture and software development in C#, R and SQL Server) in support of both disaster and day-to-day workflow for:

Missing Persons Reports data clustering Chain of Custody (evidence and body part) Crime scene investigation operational controls System utilization Time series modeling of case arrivals Probabilistic similarity and Bayesian inference techniques for body

identification and Ante mortem/Postmortem data matching. Research (SUID/SIDS, Suicides, Accidental Death Scene, etc.)

Analytics of autopsy and morgue operations, including assessments and metrics analyzing Missing Person Reports and Data Flow from 9/11 and other disaster and in-the-field mobilizations of OCME operations.

Other Experience:• Media/Digital "Big Data" management and delivery for multichannel (web,

mobile, SEM, buyer behavior, auctions, weblogs, clickstream, etc.), social, global data collection, app development, and modeling (Barclays, NBC Universal, Eikos, IPI, G4 Analytics (now Nielsen), ITC-InfoTech), and third-party data sourcing.

• Risk Management time series and machine learning algorithms for valuation, portfolio optimization, scenario and stress testing, liquidity measurement, agent based modeling and limit order market simulations. Models include multiple, non-linear, logistic and stepwise regression, clustering, time series (AR-family), EM, test design, Kalman filtering, markov models, machine learning (ANN, GA/GP, CRF), complex graph statistics, regression and classification trees, clustering methods, descriptive statistics, and more.

• Operational analytics for numerous firms.• Developed Legal Function and Counsel Analytics and Digital Data Management

Practice and PMO for attorney services. Platform develops (“big”) data sourcing and predictive analytics modeling for the legal function or counsel advisory services for risk management, such as:• Workflow and feedback loop between practice areas" (1-24-15 VY email)• Spend analysis• Text mining, document discovery• Native Application Review of Email and EDD• Total cost of resolution• Strategic M&A

Project management for topical use cases, including: Director Liability Insurance for Data Breaches Corporate Cybersecurity Insurance Polling and corporate culture and review apps Legal jurisdictions, case law, precedents, and political environment Terminations