Set Data Science vision and plan projects accordinglyCollaborate with Executive Council to maximize Data Science business valueHire and mentor data science staff towards best in class capabilities and executionStandardize tools and processes to support best practicesLead delivery of innovative data science applications in claims management, unstructured data extraction, legal document review and more

Conducted consumer insights development from questionnaire creation through insights deliveryManage 3rd party data sources for primary and secondary research, social media monitoring, and consolidated insights from acquisition through utilization Led company transformation from Gartner Analytics Maturity Level 1 to Level 3Manage Tableau server for client dashboard production and disseminationRun the Data Hub, maintaining all aspects of our AWS client including security, user provisioning, ETL, SFTP, maintenance, and billingAreas of application include: Sponsorship valuations ROI measurements Website performance reporting Marketing analytics Consumer insights

• Report Rationalization: Led the application of text analytics and unsupervised machine learning to a portfolio of 250k+ SQL scripts. This effort identified around 600 report themes that made up nearly three quarters of the volume. The results were turned over to developers in order to standardize and parameterize with the goal of freeing up a large portion of the nearly 4500 headcount producing reports to higher value activities. • Social Media Fuzzy Matching: Applied NLP on multiple social media data sources to match records without keys and classify posts. Overall accuracy exceeded 90% while minimizing impacts of false positives.• Scaled Analytics: Provided guidance to 13 person data science team to migrate existing analytic use cases into distributed processing on Hadoop cluster. The results saved 1500 data scientists labor hours annually and generated $15MM in economic value from the first use case alone.• Machine Learning Functional Lead. Developed marketing materials, training sessions, and business cases for all RCG Machine Learning engagements.• Wrote RFP responses, SOW’s, Project Plans, and pricing models.• Speaking engagements at DMA and INFORMS.

• Leveraged AT&T's vast collection of location data assets to create multiple SaaS initiatives to solve important questions regarding consumer behavior. Areas of focus included Real Estate, Segmentation, Visitor Analysis, Prospect Modeling, and more in a regulation compliant and privacy friendly format.• Applied advanced analytics to 1.8 Trillion location events to create an externally monetized data product.• Built analytic product development team from scratch hiring and placing 7 candidates.• Led the development of location data applications for internal and external use leveraging several key Big Data and programming technologies including Hadoop, Hive, Python, R, Java, and Alteryx.• Provided market assessments and competitive intelligence while creating go to market strategies.• Developed product pricing models, business cases, and P&L.• Lived in the trenches for Capex and Opex strategic planning for product lifecycle.• Delivered multiple Analytic Proofs of Concepts while building location based Big Data products that are now generating 8 figures worth of economic value and revenues.

• Maintained portfolio of core Market Mix Models ensuring they were fresh, accurate, and up-to-date. Drove incremental improvements through new data elements, tightening of statistical standards, and application of best in class methodologies.• Developed methodologies and built ad hoc models to answer such business challenges as “Long Term Impact of Media on Sales”, “Efficacy of Sponsorships, Primetime, and Cable Properties”, “Impact of Media on NPS”, “How Mandatory Commercial Elements Affect Brand Recall”, and many more.• Managed Data Governance team to hit quality and timeline requirements in the ETL of nearly 200 data feeds. Sped processing timelines by two weeks while driving data processing quality improvements to 99.5% accurate on first load.• Identified and validated value of new data series and elements by working with data vendors to incorporate new data enhancements into our models• Lead the combined Modeling and Data Governance teams consisting of 2 direct reports, 3 Vendor partners, 4 indirect reports, and over a dozen offshore resources.• Spoke at 5 conferences with topics ranging from "Importance of Data Governance" to "What Makes an Effective Commercial".

Lead the design and implementation of a reporting data base as part of a larger program to overhaul all data structures for a key client. The reporting database serves up data that satisfies 95% of routine and ad hoc reporting needs while cutting turnaround time down to hours from days and increasing accuracy at the same time. Since being launched the client has made several comments and observations regarding our improvements in data management.Introduced response modeling to a client's direct mail program and grew revenues by 244% over 2 years, ROI up 100%, while increasing response rate by 91%Consulted with clients on the design and implementation of various statistical models to support their marketing initiatives.Lead the training committee for the department of 30+ individualsManage routine and ad hoc reporting and analytics for clientsManage direct reports to ensure client satisfaction and billability

Developed an application Fraud Model using Logistic Regression to identify True Name Fraud and Synthetic Fraud applications for a major cell phone provider.Designed and built models to determine credit worthiness at application in Bankcard, Personal Loans, Telecom, and Home Equity industries. Built a custom Logistic Response model with an Approval overlay blended scorecard for a sub-prime mortgage lender. This model simultaneously decreased response processing costs and increased the number of accounts funded while lowering overall risk.

Teach MGS 3100 Business Analysis.This course provides a frame of reference for using models in support of decision making in an enterprise, then introduces some of the most commonly useful modeling approaches and principles. Topics covered include model components, optimization, time series and causal forecasting, decision analysis, Monte Carlo simulation, and quality management. The course emphasizes hands-on application of the techniques using commonly available software, and demonstrates the value of these approaches in a wide variety of functional settings.

Developed a self-adjusting seasonality model that addresses product level seasonal purchase patterns. Cluster analysis was applied to determine which markets have similar purchase patterns to provide better timing of print advertising.Maintained Market Mix Models and ran quarterly updates by re-estimating parameters.Socialized the use of statistical techniques for measuring advertising effectiveness for service initiatives. The results were used to allocate a multi-million dollar ad campaign to promote service initiatives on an ongoing basis.Clustered markets using store trade area demographics to find markets that respond to marketing messages in a similar manner.

Developed a regression model to identify over-billed warranty claims.Taught a SQL query class to 10 coworkers.Developed a Vehicle Allocation DSS. Saved $150k development cost and 75 labor hours per quarter.Forecasted vehicle unit sales for production and cash flow planning. The model had a 6% error rate.Performed inventory scenario “What if?” analysis and modeling as required by management.Redesigned the dealer order guide which allows dealers to order the various options available. The results were mentioned in Excellence magazine as the best order guide the factory has put out in 30 years.

Tested rating accuracy of rating software programmed in AS/400 mainframe and stand alone software environments.Implemented useability testing checklist for all rating software.Enhanced testing use cases to provide more confidence in rating accuracy.