Statistician Resume Baltimore, MD - Hire IT People

SUMMARY:

Certificate in Predictive Analytics and Ph.D.in Statistics, proven expertise in building and validating predictive models.
Strong knowledge of machine learning techniques (e.g. clustering, classification, regression, neural networks, multivariate statistics etc.), with the ability to generate insight from large complex data.
5 years of experience in variety of programming languages (including SAS/ R / SPSS / SQL). Background in computational statistics, mathematical modelling / machine learning (firm understanding of underlying mathematics and statistics).
Building predictive models and translating specifications to real - world decisions using more than 50K observations and machine learning tools (Rapid Miner, R, Tableau and SAS Enterprise Miner software).
Purchase probability using decision tree model: produce English rules to determine likelihood of purchase.
Risk prediction of credit cards holder using logistic regression model: produce odds of default payment.
Credit risk prediction using neural network: predict the “DO NOT LEND” category.
Predictive model for churn analysis using neural network: capture percent of churners and design effective plan for retention.
Customer profiling in geographical neighborhood using stochastic gradient boosting model: customer segmentation.
Classification of women’s choice of contraception using neural network and random forest models: high degree of accuracy achieved with random forest.
World development report text mining: frequent term extraction, visualization, clustering and topic modeling.
KPI Dashboard using Tableau: determine success rate by team and region, identify geographic expansion to boost revenue.
Frequent pattern mining using W-FP Growth modeling: deliver association rules with high confidence and high support.
Prediction of customer spending using linear regression model: score prospect customers, identify dominant attribute to guide high-end advertisement.

TECHNICAL SKILLS:

Software Skills: SQL, SAS/BASE, SAS/MACRO, SAS/SQL, SAS/EG, SAS/EM, SAS Forecast Studio, R, SPSS, Mplus, Iveware, Lisrel, RapidMiner, Tableau, Hadoop, MapReduce, Minitab, Excel, MS Access, Web design, MS Office Suites.

EXPERIENCE:

Statistician

Confidential, Baltimore, MD

Responsibilities:

Extract using SQL, transform and load large complex call center data from relational database Server.
Monitor outliers, evaluate trend.
Filter, analyze, interpret and write reports.
Merge multiple related data sets into consolidated data marts for analytical modeling, scoring, and summarization
Analyze data using SAS programming, data manipulation, management, and reporting.
Analyze data using R programming.
Provide statistical support and interact with research staff, at the center.
Collaborate with faculty for peer-reviewed publications.
Direct the planning, design, production and management of data as part of PCMH model.
Collaborate with database manager and other project staff.
Meet regularly with informatics team to monitor and evaluate the data collection, data quality and recommend improvement.
Maintain strong commitment to accuracy, detail, confidentiality and timeliness of completion.
Introduction to R
Statistics and R programming for Researchers

Statistical Consultant

Confidential

Responsibilities:

Advise clients on data collection. Perform data extraction, data cleaning, data transformation, exploratory analysis, data modeling, model selection, and residual diagnostics analysis with different data structure: univariate, multivariate, cross-sectional, repeated measures; with different variable type: discrete, continuous, and categorical.
Apply data mining techniques: Decision Tree, Random forest, Association analysis, Neural Network, Discriminant Analysis, Text Mining, and Clustering.
Build statistical models: Linear, Generalized Linear, Mixed effect, Hierarchical Linear, Non-linear, Latent Growth, Categorical, Complex survey design.
Report results with tables, graphs, and layman’s interpretation.

Assistant Professor of Statistics .

Confidential, Rochester, NY

Responsibilities:

Teach basic and advanced statistical topics; online course management and Instructional tools.
Teach micro-array data analysis with R and Bioconductor packages to students with major in Bioinformatics and Biology.
Mentor graduate and undergraduate students for thesis and research.
Collaborate with other faculty for peer-reviewed publication.

Lecturer

Confidential, Ann Arbor, MI

Responsibilities:

Teach and coordinate multiple sections of undergraduate statistics large class course
Mentor and supervise graduate student teaching assistants.
Write and manage a course website

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Statistician Resume

Baltimore, MD

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