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Statistical Analyst Consultant Resume Profile

DC

Summary

  • Excellent ability to convert analytical results into reports and to document works effectively
  • Excellent statistical modeling and forecasting skills for finding important factors that correspond to business requirements, such as consumer behavior, consumer acquisition, pricing, cause-effect, fraud detection, customer relationship, healthcare research
  • Excellent organizational, communication and interpersonal skills
  • Hands-onSAS programming skills and SQL on both UNIX and Windows
  • Strong capability to pick up on its business domain knowledge quickly and to start using the new data in different industries
  • Excellent ability to work in a cross-functional environment - a good team player
  • Know how to use statistical skills to identifying potential fraud
  • Possess Responsible and positive working attitudes.Willing to take responsibility to manage projects from the business requirement to the development of statistical models, and to maintain the statistical models
  • Proficiency in statistical procedures with the following tech. skills by using SAS/Stat.

Tech. Skills: Logistic Regression, Linear Regression, Generalized Linear Regression, Time Series Analysis, Sampling, Categorical Analysis, Design of Experiment, Multivariate Analysis, Frequency Analysis, Trend Analysis, Correlation Analysis, Statistical Testing such as t-test, chi-square test , Scoring score card , risk modeling including PD/LGD modeling, Clustering, Segmentation, CRM and Decision Trees with SAS Enterprise Miner, SQL, SAS/Stat, SAS EG, JMP, Minitab, EXCEL, Power Point, Hyperion, Toad, UNIX, etc.

Experience:

Confidential

Statistical Analyst Consultant

Providedlogistic regression for scoring, profiling, segmentation and ad hoc analysis to support marketing plans. Made recommendations for reporting and segmentation and testing for direct response campaigns.Developed profiles of various customers.Perform analyses of campaigns for test and control groups on subpopulations.

Confidential

Statistical Analyst Consultant

As a consultant, created SAS reports for different clients of different states.FamiliarMedicare, Medicaidand pharmacy rules, regulations and data. Work on SAS client-server environment and Toad database application. The output reports can be EXCEL, PDF and HTML format.

Confidential

Operations Analyst

Time Series Modeling:Time series modeling and forecasting for power, cooling and storage usages prediction of eight data centers creating monthly power point forecasts reports for strategy and process improvement. Creating and maintaining documents holding bi-weekly meetings with all data centers.

Confidential

Statistical Modeler Consultant

Programmed withSAS for the validation of converted data in very large databases by using SAS SQL through DB2 database Validated statistical models after data conversion. Tools used: SAS, UNIX, and SAS EG.

Confidential

Statistician Independent consultant

  • Built Logistic regression model from scratch to identify the important factors that shape the higher spending group of buyers and use the model to help customer representatives to bring more customers into the group
  • Developed predictive model for customer LTV with linear regression
  • Answered fundamentals questions: such as, which print Ad sources did customers come from in the initial purchase, which channels did customers come from in the initial purchase, what categories of products did customers purchase, how did coupons and discounts work in purchases
  • Educated leaders to understand the process of the statistical analysis
  • Initiated the big project in IT to write data dictionaries to describe attributes in 400 data tables so that they can be used correctly and reduce confusing and time consuming work.

Confidential

Statistical Modeler Consultant

Did univariate analysis profiling of some variables of customer data.

Didstatistical modeling to identify factors leading to inactive and dormant accounts, and to predict future accounts to prevent more inactive accounts. It looks for the important customers' behaviors and characteristics that impact accounts to be inactive or dormant.

Data management analysis tools: SAS, JMP, EXCEL,SQL, Cognos. Statistical methods:frequency analysis, correlation analysis, univariate analysis, Logistic Regression.

Confidential

  • Data management very large amount of variables and multiple data sets. Used multiple comparison to answer the questions: are test oils and test labs dependent for the oils were made from the same recipe, do they have the same test results for each test oil, does it get the same test result regardless of the test labs It used PROC GLM with LSmeans and contrast methods.
  • Analyzed and modeled highly correlated data from engine oil tests and oil formulation. Used Logistic regression to predict whether a test can pass the industry testing criteria. Discovered the important variables that impact the response variables of engine tests. Reported results of findings provided suggestions and conclusions with relative graphics plots. Analyzed extreme outputs of experimental tests for formulation chemists with JMP.

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