SUMMARY

• Around 5.5 years of professional IT experience in Machine Learning, Deep Learning, Reinforcement Learning, Statistics, Transforming Data with large datasets of Structured and Unstructured data, Data Engineering, Data Validation, Predictive modeling, Data Visualization.
• Extensive use of Natural Language Processing (NLP) for Text Analytics, developing different Statistical Machine Learning, Data Mining solutions to various business problems and generating data visualizations using Python2.7\3.4.3, R 3.2.2, SAS, SQL, PL/SQL and Tableau9.4.
• Hands on experience in implementing LDA, Naïve Bayes and skilled in Random Forests, Decision Trees, Linear and Logistic Regression, SVM, Clustering, Bagging, Boosting, Neural networks, Principle Component Analysis and good knowledge on Recommender Systems.
• Proficient in Statistical Modeling and Machine Learning techniques (Linear, Logistics, Decision Trees, Random Forest, SVM, K - Nearest Neighbors, Bayesian, XG Boost) in Forecasting/ Predictive Analytics, Segmentation methodologies, Regression based models, Hypothesis testing, Factor analysis/ PCA, Ensembles.
• Expertise in transforming business requirements into Analytical models, designing algorithms, building models, developing datamining and reporting solutions that scales across massive volume of structured and unstructured data.
• Strong experience in Software Development Life Cycle (SDLC) including Requirements Analysis, Design Specification and Testing as per Cycle in both Lean Six Sigma and Agile methodologies.
• Adept in statistical programming languages like R3.2.5, MATLAB and Python 2.7.3/3.4.3 including Big Data technologies like Hadoop, Hive.
• Experienced over translating data driven insights combined with domain knowledge into business stories and dashboards using traditional presentation tools as well as visualization tools like Tableau 9.4, R3.2.2 programing and Python 3.4.3.
• Expert in working on all activities related to the development, implementation, administration and support of ETL processes for large-scale Data Warehouses using Data Transformation Services (DTS) and (SQL Server Integration Services) SSIS with MS SQL 2014/2012/2008 R2.
• Solid understanding of RDBMS database concepts including Normalization and hands on experience creating database objects such as tables, views, stored procedures, triggers, row-level audit tables, cursors, indexes, user-defined data-types and functions.
• Experience with Data Analytics, Data Reporting, Ad-hoc Reporting, Graphs, Scales, PivotTables and OLAP reporting using SQL Server Reporting Services (SSRS).
• Good understanding of Microsoft SQL Management Studio and data load / export utilities like BTEQ, Fast Load, Multi Load, Fast Export and Performance Tuning for enhancing extraction speed of data.

TECHNICAL SKILLS

Machine Learning Algorithms: Multivariate regression analysis, SVM, Cluster Analysis, NLP, Parametric and Non-Parametric Tests and Analysis of variance technique, SVM, Polynomial regression, Cluster Analysis, Decision tress, Random Forest, Apriori, MBA, Factor Analysis, Principal Component Analysis, K Nearest Neighbors, Reinforcement Leaning

Programming Languages: Python2.7/3.4.3, R3.2.2, SQL, PL/SQL, SAS, C, C++, Java

Databases: SQL Server 2014/2012/2008 R2, Oracle 11g/10g, MS Access, DB2, Teradata

Tools: HQL, Pig, Apache Spark, Microsoft Visual Studio, Minitab, Arena, SPSS, Microsoft SQL Integration Services, Microsoft SQL Analyzing Services, Microsoft SQL Reporting Services, Oracle Database Integrator.

Environment: s: Jupyter, R Studio, Anaconda, Spyder, Python Console, Pycharm

Operating system: Windows, Linux, Unix

Data Warehousing Tools: SQL Server Business Intelligence Development - SSIS, SSAS, SSRS, Informatica, Alteryx and SAP Business Objects and Business Intelligence

BI Tools: Tableau, Tableau server, Tableau Reader, SAP Business Objects, OBIEE, QlikView, SAP Business Intelligence.

PROFESSIONAL EXPERIENCE

Confidential, Alpharetta, GA

Data Scientist

Responsibilities:

• Experience working in Data Requirement analysis for transforming data according to business requirements.
• Worked thoroughly with data compliance teams such as Data Analysts and Data Engineers to gathered require raw data and define source fields in Hadoop.
• Applied Forward Elimination and Backward Elimination for data sets to identify most statically significant variables for Data analysis such PBDIT, PBIT and PBT ratios as statically significant in Profitability ratios.
• Utilized Label Encoders in Python to create dummy variables for geographic locations to identify their impact on pre-acquisition and post acquisitions by using 2 sample paired t test.
• Worked with ETL SQL Server Integration Services (SSIS) for data investigation and mapping to extract data and applied fast parsing and enhanced efficiency by 17%.
• Developed Data Science content involving Data Manipulation & Visualization, Web Scraping, Machine Learning, Python programming, SQL, GIT and ETL for Data Extraction.
• Developed Analytical systems, data structures, gather and manipulate data, using statistical techniques.
• Designing suite of Interactive dashboards, which provided an opportunity to scale and measure the statistics of the HR dept. which was not possible earlier and schedule and publish reports.
• Provided and created data presentation to reduce biases and telling true story of people by pulling millions of rows of data using SQL and performed Exploratory Data Analysis.
• Applied breadth of knowledge in programming (Python, R), Descriptive, Inferential, and Experimental Design statistics, advanced mathematics, and database functionality (SQL, Hadoop).
• Migrated data from Heterogeneous Data Sources and legacy system (DB2, Access, Excel) to centralized SQL Server databases using SQL Server Integration Services (SSIS).
• Applied Descriptive statistics and Inferential Statistics on varies data attributes using SPSS to draw insights of data regarding providing products and services for patients.
• Utilized data reduction techniques such as Factor analysis to identify most correlated values to underlying factors of the data and categorized the variable according to factors.
• Applied Wilcoxon sign test to patient and treatment data for pre-acquisition and post-acquisition for different sectors to find the statistical significance in R programming.
• Performance Tuning: Analyze the requirements and fine tune the stored procedures/queries to improve the performance of the application.
• Developed various Tableau9.4 Data Models by extracting and using the data from various sources files, DB2, Excel, Flat Files and Bigdata.
• Handled importing data from various data sources, performed transformations using Hive, Map Reduce, and loaded data into HDFS.
• Interaction with Business Analyst, SMEs and other Data Architects to understand Business needs and functionality for various project solutions.
• Identifying and executing process improvements, hands-on in various technologies such as Oracle, Informatica, and Business Objects.
• Designed both 3NF data models for ODS, OLTP systems and dimensional data models using Star and Snowflake Schemas.

Environment: Python, Jupyter, R Programming, SPSS, SQL Server 2014, SSRS, SSIS, SSAS, Microsoft office, SQL Server Management Studio, Business Intelligence Development Studio, MS Access, Informatica, SAP Business Objects and Business Intelligence.

Confidential, Carmel, IN

Data Scientist

Responsibilities:

• Involved in gathering, analyzing & translating business requirements into analytic approaches.
• Developed scripts in Python (Pandas, Numpy) fordataingestion, analyzing and data cleaning.
• And utilized SAS for developing Pareto Chart for identifying highly impacting categories in modules to find the work force distribution and created various data visualization charts.
• Performed univariate, bivariate and multiple analysis of approx. 4890 tuples using bar charts, box plots and histograms.
• Participated in features engineering such as feature creating, feature scaling and One-Hot encoding with Scikit-learn.
• Converted raw data to processed data by merging, finding outliers, errors, trends, missing values and distributions in the data.
• Generated detailed report after validating the graphs using R, and adjusting the variables to fit the model.
• Worked on Clustering and factor analysis for classification of data using machine learning algorithms.
• Developed Descriptive statistics and inferential statistics for Logistics optimization, Value throughput data to at 95% confidence interval.
• Written MapReduce code to process and parsing the data from various sources and storing parsed data into HBase and Hive using HBase - Hive Integration.
• Created SQL tables with referential integrity and developed advanced queries using stored procedures and functions using SQL server management studio.
• Used Pandas, NumPy, seaborn, SciPy, Matplotlib, Scikit-learn, NLTK in Python for developing various machine learning algorithms and utilized machine learning algorithms such as linear regression, multivariate regression, naive Bayes, Random Forests, K-means, & KNN for data analysis.
• Used packages like dplyr, tidyr & ggplot2 in R Studio for data visualization and generated scatter plot and high low graph to identify relation between different variables.
• Worked on Business forecasting, segmentation analysis and Data mining and prepared management reports defining the problem; documenting the analysis and recommending courses of action to determine the best outcomes.
• Experience with risk analysis, root cause analysis, cluster analysis, correlation and optimization and K-means algorithm for clustering data into groups.
• Coordinate with data scientists and senior technical staff to identify client's needs.

Environment: SQL Server 2012, Python, Jupyter, R 3.1.2, MATLAB, SSRS, SSIS, SSAS, MongoDB, HBase, HDFS, Hive, Pig, Microsoft office, SQL Server Management Studio, Business Intelligence Development Studio, MS Access.

Confidential

Data Analyst

Responsibilities:

• Gathered requirements and documented requirements with Use cases in Requisite Pro and created different traceability views by MS Visio.
• Worked with Data Compliance teams to identify the most suitable source of record and outlined the data.
• Functioned with project team representatives to ensure that logical and physical ER/Studio data models were established in line with business standards and guidelines.
• Deeply analyzed the clients’ data by using SQL Server Analytic Services (SSAS).
• Completed data cleaning process to discover like taking care of missing values by utilizing strategies, like, supplanting by mean, forward/backward fill, evacuating whole rows/columns/values, expelling outliers and errors, normalizing, and scaling information in data set.
• Implemented metadata repository, maintained data quality, data cleanup procedures, transformations, data standards, data governance program, scripts, stored procedures, triggers and executed test plans.
• Worked with team by Extracting Mainframe Flat Files (Fixed or CSV) onto UNIX Server and then converting them into Teradata Tables using SQL Server Integration Services (SSIS).
• Developed visualizations and dashboards using SQL Server Reporting Services (SSRS) to present analysis outcomes in terms of patterns, anomalies, and predictions use of bar charts, Line Plots, Scatter Plots, 3D plots, and histograms and connect with Hive for generating daily reports.
• Documented the complete process flow to describe program development, logic, testing, implementation, application integration, and coding by using SQL Server Reporting Services (SSRS).
• Created customized SQL Queries using SQL Server 2008/2008R2 Enterprise to pull specifieddatafor analysis and report building in conjunction with Crystal Reports.
• Designed & developed various Ad hoc reports for different teams in Business (Teradata and MS ACCESS, MS EXCEL).

Environment: SSRS, SSIS, SSAS, SQL Server 2008/2008 R2 Enterprise, MS Visio, MS Excel, MS Project, Teradata, Crystal Reports, ER Studio, Crystal reports, and Business Objects.