SUMMARY:

• Senior analytic professional with over 18 years of professional experience in government, healthcare, marketing, insurance, banking industries and big data. Specialize in advanced analytics and statistics and mentor junior analysts. Current project involves identification and prediction of trafficking behavior related to food stamps (SNAP). Previous projects focused on analytics surrounding marketing, the Affordable Care Act (ACA) and impact to business. Able to devise innovative custom predictive analytic applications including data collection, transformation, loading and back end analysis. Core competencies include:

PROFESSIONAL EXPERIENCE:

Senior Analyst/Predictive Modeler

Confidential

• Physician analytics with SAS
• Developed eight predictive acquisition models based on physician lifecycle
• Macro development
• Practice level analysis
• Adhoc analysis

Statistician - Fraud Detection

Confidential

• Developed predictive models for Accenture’s food stamp(SNAP) fraud project
• Constructed with clustering, ‘look alike’ regression and sampling methods in SAS
• Models are used to identify trafficking behaviors in order to provide lead lists for investigators
• SAS Programming
• Prepared and conducted training for state SNAP analysts and investigators on the principles and uses of predictive modeling in R
• Ad hoc analysis of trafficker networks in SAS
• Identified geographic hotspots for trafficking activity
• Performed transaction analysis for suspect cases

Data Scientist

Confidential

• Developed big data propensity models for Confidential real time decision system.
• 25+ models based on large partner data set
• 70 million records
• 450 variables
• Constructed with clustering, regression and sampling methods
• Fully automated
• Each model has a high level of accuracy, predictive power and generates a larger lift than earlier models
• Macro development for common calculations
• SAS Programming

Technical Consultant- Fraud Detection

Confidential

• Developing real time strategies relating to fraud with SAS Enterprise Miner, Enterprise Guide and Base SAS for Confidential
• Typical data set size is 15 million records with 110 fields
• Identified and blocked approximately $2M in additional fraud
• Mentor and train other bank and fraud team members on:
• SAS Enterprise Miner
• Predictive Modeling
• Other statistical procedures as needed
• Macro development for large data extractions, data screening and reports
• SAS Programming

Managed Care Analyst

Confidential

• Analytics in support of County Care. A new healthcare plan serving Illinois' Medicaid population in Cook County Illinois. Duties included:
• Analytic database table development
• Typical data set size was approximately 1 million records
• Compared patients across clinics to identify and address potential staffing and utilization issues through:
• Claims analysis
• Clinic level analysis with Cerner
• Demographic analysis

Senior Healthcare Statistician

Confidential

• Responsible for advanced analytics and statistics with, Teradata, SAS Enterprise Guide and SAS Enterprise Miner in order to evaluate the impacts of the Affordable Care Act (ACA) at a major Chicago area healthcare company (HCSC).
• Daily use of SAS Enterprise Guide and advanced analytics
• Daily use of big data often totaling several hundred to two billion records
• Macro development for large data extractions, common calculations and reports
• Creating streamlined, easy to use code of the new Government risk adjustment model which was used in several analytic projects related to the ACA.
• Utilize chart review analysis to compare claim ICD9 to chart ICD9 codes to determine any difference and associated impact on risk scores relating to ACA.
• Ensured that the RSA outreach Team understood the relative impact of medical conditions collected during recruitment interviews. The result is creation of a guide to prioritize the order of conditions used for interviews.
• Creating analytics to support coordination between the ACA team and Health Care Management (BCC-an outreach program) to facilitate improved accuracy during diagnosis code capture.
• Designed and implemented several predictive models that target program outreach acceptance (BCC), persistency measures (built off of HHS risk model logic) and risk segmentation (built off of HHS and Verisk models).
• Created analytics when converting ICD9 to ICD10 codes that measure the impact on various management reports and associated predictive models.
• SAS Programming

Senior Statistician

Confidential, Chicago, IL

• Provided advanced statistical analysis and analytics utilizing SAP, SQL, Base SAS, Enterprise Guide and SAS Stat in a business to business environment Detailed analysis of maintenance repair products such as fasteners and chemicals.
• Daily use of big data up to 50 million records with Enterprise Guide and Base SAS
• Sku level analysis
• Pricing analysis
• Macro development for common calculations and reports
• Identified over $25 Million in new business opportunities for use by field sales agents
• Developed GIS models for pilot markets to assist in territory management
• Constructed a predictive model with SAS Stat for high value account identification which lead to targeted outreach by marketing
• Supported lean activities in order to reduce inventory and transportation costs
• Statistical process analysis
• Mentored junior analysts in SAS and predictive regression models
• Consulted with senior management to develop predictive models and statistical analysis for agent performance
• Ad hoc SAS Programming

Confidential

Senior Analyst

• Responsible for SAS and SQL programming, analytic research, leadership and predictive modeling for CPG, loyalty and direct marketing programs with primary and secondary data sources.
• Team and individual consulting from ‘Cradle to Grave’ on various projects. ‘Heads up’ analytics including segmentation, Email analysis, trend, market basket analysis,
• Hypothesis and Significance Testing, and marketing tests (champion/challenger) for national and international clients.
• Heavy use of SAS, SQL server and CHAID for:
• Sku level analysis
• Loyalty marketing analysis

Confidential

Senior Statistician/ SAS programmer

• Individually conceived, designed and implemented a high level predictive risk model for catastrophic disease interventions. This multi-dimensional methodology which took advantage of a large ORACLE claims database (ICD9 codes and pharmaceutical) proved superior to conventional disease methodology in terms of predictive power and precision. 
• Automated reports based on the predictive model results
• Macro development for common calculations
• Delivered several well received presentations to members of academia and other catastrophic disease management professionals regarding predictive modeling applications
• Statistical Consulting with medical claims within the organization and among clients:

Confidential

Decision Support Analyst/Statistician/ SAS programmer

• Developed predictive models for membership
• Churn models
• Acquisition models
• $8.8MM in membership opportunities
• Individually developed and implemented predictive models for direct medical product (CPT, etc.) marketing (both response and churn).
• Identified over $4.2MM in marketing program savings
• Macro Development for common calculations
• Individually conceived, designed and implemented a lifetime value analysis of physicians through the use of the use of the AMA Masterfile, other internal and external (partner) data sources.
• Identified $240MM in membership and other marketing opportunities
• Survey duties
• Measure public health sentiments among physicians
• Measure acceptance and perceived value of continuing education programs
• Statistical Consulting on a variety of subject matter within the organization and among marketing partners.

TECHNICAL SKILLS:

Analytic Skills:  Multivariate methods - such as OLS and logistic regression, principal components, structural equation modeling, factor analysis, discriminant analysis, multiple comparisons. Macro Programming Proc SQL/SQL TREE Modeling (e.g. CHAID, CART, etc.) Hypothesis testing Significance testing Categorical and Survey analysis Data mining- e.g. predictive modeling, decision trees, segmentation, large datasets Geographic Information Systems (GIS) Time-series methods-such as forecasting and time-series regression. Johns Hopkins ACG software