SUMMARY:

• SAS - All Versions Through 9.4
• BASE, STAT, GRAPH, Connect, Access, ETS, OR, QC, FSP, AF, SCL, ODS
• Enterprise Miner and Enterprise Guide
• Expert level of SAS data step, macro, and AF programming.
• Scalable Performance Data (SPD) Server
• I have used and developed with SAS under Windows, Unix & Linux, Grid Environment (RH Linux), Mainframe Batch & TSO
• Programming Languages and Environments
• SAS: Base, Macro, Proc FCMP, Proc DS2, Proc SQL
• Python - Core Language, Pandas, Numpy, SciPy, SqlAlchemy
• Environments and Editors: PyCharm, Spyder, Rodeo, Jupyter Notebooks, MS Visual Studio w/ Python Tools
• R: Core Language, DpLyr
• Environments and Editors: R Studio, Jupyter Notebooks, MS Visual Studio w/ R Tools
• JavaScript
• Application Development with Visual Basic .NET and Visual C# Professional
• Visual Basic for Applications (Excel, Word, PowerPoint) Versions
• Web Automation and Scraping using Selenium with Python and C#
• Unix/Linux shell scripting with the Korn Shell (KSH), Perl, and Python
• Mainframe JCL, TSO, CLIST, REXX
• Analytic and Data Mining Software
• SAS/STAT, SAS/ETS, SAS/QC, and SAS Enterprise Miner
• Salford Systems CART, TreeNet, and Random Forests
• Open Source Data Mining Software: Rapid Miner, WEKA, KNIME, Orange
• XGBoost from R, Python, and Command Line
• Microsoft LighGBM from R, Python, and Command Line
• Python: Numpy, ScyPy, MatPlotLib, Pandas
• Databases
• SQL Server - Oracle - Teradata - Netezza - IBM DB2 - SAS SPDS - AWS Redshift - PostgreSQL
• Other Software
• Microsoft Access, Excel, Word, PowerPoint, OneNote, Outlook, Project, and Visio

PROFESSIONAL EXPERIENCE:

Python Developer Contractor

Confidential

• Convert SAS scripts to the Python equivalent as part of a project to move a data warehouse to the cloud (AWS Redshift) and move away from using SAS
• Develop new Python scripts for data quality tests and reports for the new Redshift data warehouse.

DAta Scientist Senior Manger

Confidential

• Provided support for Data Scientists and Actuaries utilizing SAS.
• Gathering and Preparation of data for developing statistical and machine learning models
• Historical scoring of statistical and machine learning models
• Ad-Hoc reporting and data analysis
• Developed and Implemented a SAS ETL and Model Scoring Application for Claims Adjusters.
• ETL development and implementation extracting data from multiple DB2 databases, transforming the data in SAS, and loading data to an Oracle database.
• Statistical Model Integration - Scoring and Monitoring
• Managed a SAS Programmer Contractor for a 6-month period who helped with development and implementation of new features

SAS Programmer, Data Analyst,

Confidential

• Worked on a variety of different projects including data cleansing and preparation data analysis web automation and scraping using Python and Selenium
• SAS application development for production ETL and scoring models
• Windows application development using Visual Studio 2010 and VB.NET and Open XML SDK 2.5 for Office
• Form Generation Application for Commercial Property Policies in the US, Canada, Britain, Ireland, and South Africa.
• Data Retrieval Application for Turkey Government Citizen Data

Senior Statistician Manager

Confidential

• Internal consulting across the company for
• Credit bureau data and creation of custom credit bureau summary attributes ptimal use of our internal data sources
• SAS Programming and Application Development
• Custom Bureau Summary Attributes
• Designed and built process and tools to create, manage, and calculate custom credit bureau summary attributes using internal raw trade line level bureau data.
• Approximately 1500 attributes across the 3 main credit bureaus
• Developed applications and training to make it easier for statisticians to access and use internal data
• Developed a SAS mainframe application with a Windows GUI front end to import raw bureau data into SAS and calculate a large set (1500+) of summary variables. This was done for all 3 of the main credit bureaus.
• Developed a SAS application for residual analysis and monitoring of forecast models, utilizing CART and TreeNet for some of the analytics and SAS for the data processing and reporting.
• Implemented a 3rd party mortgage valuation and forecast scoring platform. My role on the project was as a subject matter expert and as a technical communication bridge between the analytic business group and IT.

SENIOR STATISTICIAN MANAGER

Confidential

• Managed a team of 3 statisticians and one contractor statistician/sas programmer
• My team was focused on managing credit risk and direct mail marketing response. Four models were developed in this period.
• We also supported a team of 10 business analysis on many other projects that leveraged statistical analysis and techniques
• All data needs that required data not in a database were managed by myself and executed by a contractor.

SENIOR STATISTICIAN MANAGER

Confidential

• Developed and implemented 1 non-credit bureau response model that was used by one of the core business to book an additional $150MM in out standings a year
• Identified 2 additional opportunities to use non-credit bureau data to better predict risk and direct mail marketing response.
• Worked on several other efforts including postal processing and response benefits, benefit of occupation information from various data providers, and market penetration analysis.
• Integral member of a very large project to build a Marketing Acquisition prospect pool database and execution system
• Designed the process for implementing, testing, and maintaining statistical models in the system
• I was a subject matter expert of campaign execution and worked with Process Analysts to develop the system infrastructure and processes for all marketing functions

STATISTICIAN MANAGER & SAS Programmer

Confidential

• Managed 2 direct reports during this time. A project manager and a credit analyst.
• Built and implemented 3 credit card risk models for the Young Adult marketing segment.
• Lead a cross functional group of 3 to investigate, evaluate, and bring new statistical and data mining tools during this period. The result was two new tools for our statisticians and analysts.
• Increased collaboration and knowledge management in the statistician community through new tools and practices
• Provided analysis and data processing support to a team of 10 statisticians
• Coded and executed the marketing campaigns for solicitation of new credit card accounts
• From a list of names with credit bureau information determined who to mail credit card offers to
• Model coding and scoring was part of the campaign execution
• Analyzed the list for data quality and distribution shifts from previous campaigns