SUMMARY

• 6 Plus years of relevant work experience as a Data Scientist, including deep expertise and experience with statistical data analysis such as linear models, multivariate analysis, Statistical Analysis, Data Mining and Machine Learning Skills.
• High Proficiency in Advanced Excel and Access including complex data analysis and manipulation.
• Data Driven and highly analytical with working knowledge and statistical model approaches and methodologies (clustering, Segmentation, Variable reduction, Regression analysis, Hypothesis testing, Decision trees, Machine learning), rules and ever evolving regulatory environment.
• Experience with analyzing online user behavior, Conversion Data(A/BTesting) and customer journeys, funnel analysis.
• Strong Data Analysis skills using business intelligence, SQL and / or MS Office Tools.
• Experience in Agile Methodologies.
• Experience in applying predictive modeling and machine learning algorithms for analytical reports.
• Profound Analytical and problem solving skills along with ability to understand current business process and implement efficient solutions to issues/problems.
• Experience using technology to work efficiently with datasets such as scripting, data cleansing tools, statistical software packages.
• Strong understanding of how analytics supports a large organization including being able to successfully articulate the linkage between business objectives, analytical approaches &findings and business decisions.
• Professional working experience in Machine Learning algorithms such as LDA, linear regression, logistic regression, Naive Bayes, Decision Trees, Clustering, and Principle Component Analysis.
• Excellent analytical skills with demonstrated ability to solve problems.
• Mastery of R programming/data processing experience knowledge in SPSS.
• Hands on experience in writing queries in SQL and R to extract, transform and load (ETL) data from large datasets usingDataStaging.
• Experience with traditional analytics tools (Excel, Tableau and Qlikview).
• Working experienced of statistical analysis using R, SPSS, Matlab and Excel.
• Ability to work with large transactional databases across multiple platforms (Teradata, Oracle, HDFS, SAS).
• Deep understanding of Software Development Life Cycle (SDLC) as well as Agile/Scrum methodology to accelerate Software Development iteration.
• Good oral and written communication skills.
• Strong interpersonal skills to successfully build long - term relationships with colleagues and business partners.
• A results-driven individual with a passion for data/analytics who can work collaboratively with others to solve business problems that drive business growth.
• Demonstrated leadership and self-direction. Demonstrated willingness to both teach others and learn new techniques.
• Ability to work with managers and executives to understand the business objectives and deliver as per the business needs and a firm believer in team work.

TECHNICAL SKILLS

Programming & Scripting Languages: R, C, C++, JAVA, JCL, COBOL, HTML, CSS, JSP, Java Script

Database: SQL, MySQL, MS Access, Oracle

Statistical Software: SPSS, R, SAS

Web Packages: Google Analytics, Adobe Test & Target, Web Trends

Development Tools: R Studio, Notepad++

Writing Tools: Latex

Packages: Dplyr, rjson, GGPLOT2

Techniques: Machine learning, Regression, Clustering, Data mining.

Machine Learning: Naive Bayes, Decision trees, Regression models, random forests, Time-series, K-means, Association Rules, Text Analysis and Time Series Analysis.

Business Analysis: Requirements Engineering, Business Process Modeling & Improvement, Financial Modeling

Operating Systems: Microsoft windows 7/8/8.1/10/Vista/XP, Linux (Ubuntu)

PROFESSIONAL EXPERIENCE

Confidential, Baltimore, MD

Data Scientist

Responsibilities:

• Work independently or collaboratively throughout the complete analytics project lifecycle including data extraction/preparation, design and implementation of scalable machine learning analysis and solutions, and documentation of results.
• Performed statistical analysis to determine peak and off-peak time periods for ratemaking purposes
• Conducted analysis of customer data for the purposes of designing rates identified root causes of problems, and facilitated the implementation of cost effective solutions with all levels of management
• Application of various machine learning algorithms and statistical modeling like decision trees, regression models, neural networks, SVM, clustering to identify Volume using scikit-learn package in python, Matlab
• Worked on different data formats such as JSON, XML and performed machine learning algorithms in Python.
• Hands on experience in implementing LDA, Naive Bayes and skilled in Random Forests, Decision Trees, Linear and Logistic Regression, SVM, Clustering, neural networks, Principle Component Analysis.
• Performed K-means clustering, Multivariate analysis and Support Vector Machines in R.
• Work independently or collaboratively throughout the complete analytics project lifecycle including data extraction/preparation, design and implementation of scalable machine learning analysis and solutions, and documentation of results.
• Partner with technical and non-technical resources across the business to leverage their support and integrate our efforts.
• Partner with infrastructure and platform teams to configure, tune tools, automate tasks and guide the evolution of internal big data ecosystem; serve as a bridge between data scientists and infrastructure/platform teams.
• Worked on Text Analytics and Naive Bayes creating word clouds and retrievingdatafrom Twitter and other social networking platforms
• Pro-actively analyze data to uncover insights that increase business value and impact.
• Support various business partners on a wide range of analytics projects from ad-hoc requests to large-scale cross-functional engagements
• PreparedDataVisualization reports for the management using R
• Approach analytical problems with an appropriate blend of statistical/mathematical rigor with practical business intuition.
• Hold a point-of-view on the strengths and limitations of statistical models and analyses in various business contexts and is able to evaluate and effectively communicate the uncertainty in the results.
• Work independently or collaboratively throughout the complete analytics project lifecycle including data extraction/preparation, design and implementation of scalable machine learning analysis and solutions, and documentation of results.
• Performed statistical analysis to determine peak and off-peak time periods for ratemaking purposes
• Conducted analysis of customer data for the purposes of designing rates identified root causes of problems, and facilitated the implementation of cost effective solutions with all levels of management
• Application of various machine learning algorithms and statistical modeling like decision trees, regression models, SVM, clustering to identify Volume using scikit-learn package in R.
• Worked on different data formats such as JSON, XML and performed machine learning algorithms in Python.
• Approach analysis in multiple ways in order to evaluate approaches and compare results.

Confidential

Data Scientist

Responsibilities:

• Responsible for the Study/Creation of SAS Code, SQL Queries, Analysis enhancements and documentation of the system.
• Used R, SAS and SQL to manipulate data, and develop and validate quantitative models.
• Brainstorming sessions and propose hypothesis, approaches and techniques.
• Created and optimized processes in the Data Warehouse to import, retrieve and analyze data from the CyberLife database.
• Analyzed data collected in stores (JCL jobs, stored-procedures and queries) and provided reports to the Business team by storing the data in excel/SPSS/SAS file.
• Performed Analysis and Interpretation of the reports on various findings.
• Prepared Test documents for zap before and after changes in Model, Testand Production regions.
• Responsible for production support AbendResolution and other production support activities and comparing the seasonal trends based on the data by Excel.
• Used advanced Microsoft Excel functions such as pivot tables and VLOOKUPin order to analyze the data and prepare programs.
• Successfully implemented migration of client’s requirement application from Test/DSS/Model regions to Production.
• Prepared SQL scripts for ODBC and Teradata servers for analysis and modeling.
• Provided complete assistance of the trends of the financial time series data.
• Various statistical tests performed for clear understanding to the client.
• Implemented procedures for extracting Excel sheet data into the mainframe environment by connecting to the database using SQL.
• Provided training to Beginners regarding the CyberLife system and other basics.
• Complete support to all regions. (Test/Model/System/Regression/Production).
• Actively involved in Analysis, Development and Unit testing of the data.
• Generated reports of more than 100 Agent Fraud Investigation cases based on the client requirement and making sure the data is accurate.
• Complete delivery assurance of the project.

Confidential

Jr. Data Scientist

Responsibilities:

• Prepared regular patient reports by collecting samples of Diagnosed Patients.
• Cleaned data by analyzing and eliminating duplicate and inaccurate data (outliers) using R.
• Trained in Basics of Data Scientist and implemented those software applications in collecting and managing patient data in Excel/SPSS.
• Assisted in performing statistical analysis of the data and storing them in a database.
• Converted various SQL statements into stored procedures thereby reducing the number of database accesses.
• Worked with Quality Control Teams to develop Test Plan and Test Cases.
• Responsible for architecting analytic frameworks for data mining, ETL, analysis, and reporting under the supervision of the Manager.
• Involved in designing and implementing the data extraction (XML DATA stream) procedures.
• Generated graphs and reports using ggplot inRStudio for analyzing models.
• Generating the Results and predicting the accuracy.