Data Scientist Resume

PROFESSIONAL PROFILE:

A Confidential Veteran who is a strategic, focused, business oriented and results - driven Data Scientist with a strong data science background and detailed experience in Machine Learning, Big Data Analytics and Statistics.
Excellent communication, presentation and organizational skills.
Extensive experience applying analytics in gaining competitive advantage for the business and explaining results of data analytics to non-technical audience.
He has experience in building and developing data-pipeline and algorithms for Confidential, Recommendation, Clustering, Classification, and Unstructured Information Search & Natural Language Processing. Possess Top Secret/Sensitive Compartmented Information security clearance.

TECHNICAL PROFICIENCIES AND TECHNOLOGIES:

Machine Learning: classification, regression, Deep Learning and Artificial Neural Networks

Statistical Methods: Time series, Regression models, Clustering, Spatial Models, Correspondence analysis and dimensionality reduction (Principal Component Analysis, Forward and Backward Selection, LASSO and Adaptive LASSO).

Software and Programming Languages: Python (scikit-learn, matplotlib, seaborn, NumPy, statsmodel, and pandas), Hadoop (Hive, Sqoop, Flume), Apache Spark (SparkSQL, SparkML), SAS-Enterprise Miner, SAS Enterprise Guide, Base SAS, SAS Forecasting Studio, R, JMP, SQL, Linux, SQL Server 2012, Business Intelligence Platforms (Tabluea, MicroStrategy), Microsoft Excel

Selected Coursework: Methods in Time Series Analysis, SAS Programming, Stochastic Processes, Regression Analysis, Multivariate Methods, Machine Learning, Project Management, Optimization-Linear, Non-Linear, Integer Linear Programming Methods, Spatial Statistics, Natural Language Processing.

PROFESSIONAL EXPERIENCE:

Data Scientist

Confidential

Responsibilities:

Performed explanatory data analyses, prepared and analyzed historical data and identified patterns
Performed machine learning, natural language processing, and statistical analysis methods, such as classification, collaborative filtering, association rules, sentiment analysis, opinion Mining, time - series analysis, regression, statistical inference, and validation methods
Compiled data from several different sources and determine feasibility of including various metrics as variables in forecast models
Build recommendation engines, spam classifiers, sentiment analyzers and classifiers for unstructured and semi-structured data
Analyzed and modeled structured data using advanced statistical methods and implemented algorithms needed to perform analyses
Used Hadoop and related tools (Hive, Apache Spark) to manage the analysis of large data sets from different business units
Used SQL for cleaning large data sets for data analyses and used SQL for querying and performance optimization of relational databases (SQL Server 2012) Developed and updated statistical models to predict attrition and customer value calculation engine.
Ad Hoc reports for marketing, finance, planning, supply, risk and regulatory departments.
Optimization of information management by understanding evolving business needs and technology capabilities.
Promote shared infrastructure and Big Data applications to reduce costs and improve efficiency
Developed a forecast model for predicting utilization across several locations using time series and spatial statistics. Model was within 5% accuracy.

Confidential

Data Scientist

Responsibilities:

Participated in building predictive models to predict sepsis, trauma, EWS, LOS, 30-day unplanned readmission, opioid overdose, jail recidivism, asthma, and pre-term births, etc.
Conducted research, project preparation, exploring the scope, depth, and challenges of new projects, and formulating strategies and action plans.
Coordinated the DS team to ensure that the quality of the model development and the analysis of data adhere to the highest statistical standards.
Worked with AWS or Isthmus to put predictive models into production through healthcare sytem APIs.
Took initiatives to conduct in-depth assessment and selection of most appropriate methodologies for the project given its complexity, scope, and data structure for statistical analysis, exploratory data analyses, including graphs, schematic summaries, and necessary transformations and confirmatory data analyses, including statistical tests and/or modeling.
Led the design of research protocols including sample size calculations and randomization.
Participated in formulating data/analytics core-related strategies, action plans, steps and /development plans.

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