SUMMARY

• Above 7+ years of experience as Senior Data Scientist/Data Analyst in Architecture, Identity Innovation Opportunities, Interpreting & Analyzing in a Fast - Paced Environment
• Extensive experience in Machine Learning, Data mining with large data sets of Structured and Unstructured data, Data Acquisition, Data Validation, Predictive modeling, Data Visualization.
• Extensive experience in Text Analytics, developing different Statistical Machine Learning, Data Mining solutions to various business problems and generating data visualizations using R, Python and Tableau.
• Expertise in transforming business requirements into analytical models, designing algorithms, building models, developing data mining and reporting solutions that scales across massive volume of structured and unstructured data.
• Experience in designing stunning visualizations using Tableau software and publishing and presenting dashboards, Storyline on web and desktop platforms.
• Designing of Physical Data Architecture of New system engines.
• Experience with NoSQL databases, such as MongoDB, Cassandra, HBase.
• 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 and good knowledge on Recommender Systems.
• Proficient in Statistical Modeling and Machine Learning techniques (Linear, Logistics, Decision Trees, Random Forest, SVM, K-Nearest Neighbors, MLbase, Bayesian, XG Boost) in Forecasting/ Predictive Analytics, Segmentation methodologies, Regression based models, Hypothesis testing, Factor analysis/ PCA, Ensembles.
• Strong experience in Software Development Life Cycle (SDLC) including Requirements Analysis, Design Specification and Testing as per Cycle in both Waterfall and Agile methodologies.
• Adept in statistical programming languages like R and also Python including Big Data technologies like Hadoop, Hive.
• Skilled in using dplyr and pandas in R and python for performing exploratory data analysis.
• Experience with structured or un-structured data analysis and tools (SQL, Hadoop, Spark, NoSQL, MYSQL, Hive, Pig, etc.
• Adept in using libraries such as pandas, numpy, seaborn, scipy, matplotlib, scikit-learn, BeautifulSoup, MDP, Orange, Rpy2, LibSVM, neurolab, NLTK.
• Proficient in managing entire data science project life cycle and actively involved in all the phases of project.
• Skilled in performing data parsing, data manipulation and data preparation with methods including describe data contents, compute descriptive statistics of data, regex, split and combine, Remap, merge, subset, reindex, melt and reshape.
• Good knowledge in Data Analysis, Data Validation, Data Cleansing, Data Verification and identifying data mismatch
• Extensive experience in data acquiring, merging, cleaning, analyzing and mining structured, semi-structured and unstructured data sets.
• Good understanding of Teradata SQL Assistant, Teradata Administrator and data load/ export utilities like BTEQ, Fast Load, Multi Load, Fast Export.
• Experienced in SQL queries and optimizing the queries in Oracle, SQL Server, DB2, and Netezza & Teradata.
• Experience in modeling with both OLTP/OLAP systems and Kimball and Inmon Data Warehousing environments.
• Good industry knowledge, analytical and problem solving skills and ability to work well within a team as well as an individual.

TECHNICAL SKILLS

DataScience tool: R3.4.2/3.x, Python3.0, MATLAB

Database: Oracle 11g/12c, MS Access, SQL Server 2012/2014, Sybase and DB2, Teradata14/15, Hive

Machine Learning: Linear regression, Logistic regression, Decision tree, Random

DataModeling Tools: Erwin r9.6/9.5, ER/Studio 9.7, Star-Schema Modeling, Snowflake-Schema Modeling, FACT and dimension tables, Pivot Tables.

BI Tools: Tableau 7.0/8.2, Tableau server 8.2, Tableau Reader 8.1,SAP Business Objects, Crystal Reports

Languages: SQL, PL/SQL, ASP, Visual Basic, XML, Python, SQL, T-SQL, SQL Server, C, C++, JAVA, HTML, UNIX shell scripting, PERL

Applications: Toad for Oracle, Oracle SQL Developer, MS Word, MS Excel MS Power Point, Teradata.

BigData: Hadoop, Spark, Hive, Cassandra, MongoDB, MapReduce, Sqoop, Hive.

Operating Systems: Microsoft Windows 9x / NT / 2000/XP / Vista/7 and UNIX.

Methodologies: Agile, RAD, JAD, RUP, UML, System Development Life Cycle (SDLC), Ralph Kimball and Bill Inmon's, Waterfall Model.

Operating System: Windows, Unix, Sun Solaris

PROFESSIONAL EXPERIENCE

Confidential - Durham, NC

Sr. Data Scientist

Responsibilities:

• Worked as Data Scientist and used predictive modeling, statistics, Machine Learning, Data Mining, and other aspects of data analytics techniques to collect, explore, and extract insights from structured and unstructured data
• Responsible for data identification, collection, exploration & cleaning for modeling, participate in model development
• Researched, evaluated,architect, and deployed new tools, frameworks and patterns to build sustainable BigDataplatforms for our clients.
• Utilized machine learning algorithms such as linear regression, multivariate regression, Naive Bayes, Random Forests, K-means, & KNN for data analysis.
• Advanced Text analytics using Deep learning techniques such as Convolution neural networks to determine the sentiment of texts.
• Natural Language Processing to understand text content on including reviews, descriptions and interactions between users on our marketplace.
• Developed POC in Spark to Query the dataset.
• Understand transaction data and develop Analytics insights using Statistical models using Machine learning.
• Involved in gathering requirements while uncovering and defining multiple dimensions. Extracted data from one or more source files and Databases.
• Used Spark for testdataanalytics using MLLib and Analyzed the performance to identify bottlenecks.
• Designed data processing pipelines with a combination of the following technologies: Hadoop, Map Reduce, Spark, Hive, Kafka, Avro, SQL and NoSQL data warehouses.
• Converted the unstructured data into structured data using Apache Avro.
• Designed predictive models using the machine learning platform -H2O, Flow UI.
• Used the Agile Scrum methodology to build the different phases of Software development life cycle.
• Developed MapReduce/Spark modules for machine learning & predictive analytics in Hadoop on AWS
• Architected overall master data hub for data elements that are used by multiple IT systems.
• Defined the architecture and various phases for the implementation of the transactional anddata warehouse systems.
• Utilized various new supervised and unsupervised machine learning algorithms/software to perform NLP tasks and compare performances.
• Created reports with Crystal Reports and scheduled to run on a daily basis.
• Accomplished multiple tasks from collecting data to organizing data and interpreting statistical information.
• Evaluate the performance of various algorithms/models/strategies based on the real world data sets.
• Interacted with the other departments to understand and identify data needs and requirements and work with other members of the IT organization to deliver data visualization and reporting solutions to address those needs

Environment: Erwin 9.6, Apache Spark 2.0.2, MLbase, Oracle 12c, Teradata 15, SAS, ODS, Agile, MapReduce, regression, logistic regression, random forest, neural networks, Avro, Topic Modeling, NLTK, XML, MLLib & Json.

Confidential - Chicago, IL

Sr. Data Scientist

Responsibilities:

• Lead the full machine learning system implementation process: Collecting data, model design, feature selection, system implementation, and evaluation
• Fulfilled all data science duties for a high-end at healthcare and operated with Data Scientists and Product Managers to frame a problem, both mathematically and within the business context.
• Establish a robust process by machine learning(MLbase) to insure the predictive analytics and quality of all algorithms and processes
• Ensure operational and optimal execution of production data science routines and processes
• Implemented data mining, and predictive analytics to correlate the big data sets and events, and derive dynamic cyber security rules.
• Performed data cleaning and data preparation tasks to convert data into a meaningful data set using R
• Worked on AWS S3 buckets and intra cluster file transfer between PNDA and s3 securely.
• Cleaned data using R, then visualize the data, and derive statistical modeling plots
• Used R to identify product performance via Classification, tree map and regression models along with visualizing data for interactive understanding and decision making.
• Applied statistical modeling techniques on the queried datasets such as regression analysis and curve fitting techniques.
• Coded R functions to interface with CAFFE Deep Learning Framework.
• Worked on Data Ingestion using Sqoop from Oracle to HDFS.
• Performed in depth analysis ofdata& prepare daily reports using SQL, MS Excel, MS PowerPoint and share point.
• Independently coded new programs and design Tables to load and test the program effectively for the given POC's using Big Data/Hadoop.
• Design and developed advance analytical methods on client data for link analysis, natural language processing, decision trees and risk scoring
• Designed cubes fordatavisualization with parameterization and cascading.
• Developing full life cycle software including defining requirements, prototyping, designing, coding, testing and maintaining software.
• Implemented supervised learning algorithms such as Neural networks, SVM, Decision trees and Naïve Bayes for advanced text analytics.
• Using Data mining models techniques to get satisfactory demanding products for customers
• Collect data from various databases and cleaning data for statistical analysis and model
• Used R to develop regression modeling for data analysis
• Interpreted complex simulation data using statistical methods.
• Designed various reports using Pivot - tables, and Pivot chart charts like Bar, Pie, Line etc.

Environment: R3, HBase, AWS, NoSQL, QlikView, PL/SQL, Tableau 8.0, MLbase, Oracle 11g, JSON, Hadoop (HDFS), PIG, Sqoop, MAHOUT, neural networks, Hive.

Confidential - Princeton, NJ

Data Scientist

Responsibilities:

• Participated in all phases of data mining; data collection, data cleaning, developing models, validation, visualization and performed Gap analysis.
• Performed K-means clustering, Multivariate analysis and Support Vector Machines in Python.
• Used Pandas, Numpy, Seaborn, Scipy, Matplotlib, Scikit-Learn, NLTK in Python for developing various machine learning algorithms (MLbase).
• Developed Clustering algorithms and Support Vector Machines that improved Customer segmentation and Market Expansion.
• Data Story teller, Mining Data from different Data Source such as SQL Server, Oracle, Cube Database, Web Analytics, Business Object and Hadoop
• Data Manipulation and Aggregation from different source using Nexus, Toad, Business Objects, Power BI and Smart View
• Developed Clustering algorithms and Support Vector Machines that improved Customer segmentation and Market Expansion.
• Developed analytics and strategy to integrate B2B analytics in outbound calling operations
• Programmed a utility in Python that used multiple packages (scipy, Numpy, pandas)
• Implemented analytics delivery on cloud-based visualization such as Business Object and Google analytics platform
• SPOC Data Scientist and predictive analyst to create annual and quarterly Business forecast reports.
• Responsible for defining the testing procedures, test plans, error handling strategy and performance tuning for mappings, Jobs and interfaces.
• Created entity relationship diagrams and multidimensional data models, reports and diagrams based on the requirements.
• Gathering all the data that is required from multiple data sources and creating datasets that will be used in analysis.
• Perform data cleaning and transformations that is suitable for applying models.
• Perform a proper EDA, Univariate and bi-variate analysis to understand the intrinsic effect/combined effects.
• Worked on multiple predictive models to predict future electricity and gas bills based on the current usage patterns.
• Automate the process of performing stepwise regression and model training based on AIC values and P-values.
• Perform cross-validation to check MAPE values to self-train the model.
• Performed statistical tests and applied regression techniques to predict the power.
• Created dash boards and visualization on regular basis using ggplot2 and Tableau
• Creating customized business reports and sharing insights to the management
• Responsible for planning & scheduling new product releases and promotional offers
• Parsing data, producing concise conclusions from raw data in a clean, well-structured and easily maintainable format.
• Worked on Text Analytics, Naive Bayes, Sentiment analysis, creating word clouds and retrieving data from Twitter and other social networking platforms.

Environment: Erwin 9.5, Tableau 8.0, Json, MDM, MLLib, PL/SQL, Teradata 14.1, Spark, R Studio, MAHOUT, AWS, HDFS, NoSQL.

Confidential - Tampa, FL

Sr. Data Analyst/Data Scientist

Responsibilities:

• Worked with Business users for requirements gathering, business analysis and project coordination.
• Data blending implemented on databases and generated interactive dashboards in Tableau Portal
• Worked on data related to enterprise data quality in order to analyze data flow models in JD Edwards’s environment.
• Researched on Multi-layer classification algorithms as well as building Natural Language Processing model through ensemble.
• Executed and validated data transformations in target system with SQL.
• Published Workbooks by creating user filters so that only appropriate teams can view it.
• Work with customers to define reporting needs and implementTableaureports to satisfy needs
• Worked on data cleaning and reshaping, generated segmented subsets using Numpy and Pandas in Python.
• Developed Python scripts to automate data sampling process.
• Ensured the data integrity by checking for completeness, duplication, accuracy, and consistency.
• Worked on model selection based on confusion matrices, minimized the Type II error.
• Generated cost-benefit analysis to quantify the model implementation comparing with the former situation.
• Continuously collected business requirements during the whole project life cycle.
• Identified the variables that significantly affect the target
• Used cascaded parameters to generate a report from two different Data Sets.
• Involved withQuery Optimizationto increase the performance of the Report.
• EmployedFiltered Indexesto improve query performance and reduce the storage and maintenance cost.
• Generated data analysis reports using Matplotlib, Tableau, successfully delivered and presented the results for C-level decision makers.
• Design and create daily, monthly and ad-hoc reports for BI and Analytics customers
• Provide recommendations for data presentation within reports/dashboards
• Support for end users connected to servers in various departments regarding connection and data access issues.
• Involved in defining the business/transformation rules applied for sales and service data.
• Played a major role in production support of SSAS cube and SSIS jobs.
• Involved in writing MDX queries and performance optimization of the SSAS cubes.
• ImplementedIncremental load, usedEvent Handlersto clean the data from different data sources
• Wrote and optimized complex SQL queries involving multiple joins and advanced analytical functions to perform data extraction and merging from large volumes of historical data stored in Oracle 11g, validating the ETL processed data in target database.
• Conducted model optimization and comparison using stepwise function based on AIC value
• Data blending implemented on databases and generated interactive dashboards.
• Published Workbooks by creating user filters so that only appropriate teams can view it.
• Embedded Tableau views in to SharePoint
• Analyzed the source data and handled efficiently by modifying the data types.
• Generated tableau dashboards for sales with forecast and reference lines.
• Created SSIS packages to clean and load data to data warehouse.
• Created package to transfer data between OLTP and OLAP databases.
• Created SSIS Packages using Pivot Transformation, Fuzzy Lookup, Derived Columns, Condition Split, Term extraction, Aggregate, Execute SQL Task, Data Flow Task, and Execute Package Task etc. to generate underlying data for the reports and to export cleaned data from Excel Spreadsheets, CSV files to data warehouse.
• Applied various machine learning algorithms and statistical modeling like decision tree, logistic regression, Gradient Boosting Machine to build predictive model using scikit-learn package in Python.

Environment: Python, Oracle, MS Excel, SSIS, SSAS, Tableau, Sql, ETL, decision tree, logistic regression, Gradient Boosting Machine, scikit-learn, OLAP & OLTP, SQL*Loader.

Confidential

Data Analyst

Responsibilities:

• Gathered and translated business requirements into detailed, production-level technical specifications, new features, and enhancements to existing technical business functionality.
• Used the Waterfall methodology to build the different phases of Software development life cycle.
• Conducted JAD sessions with management, SME (Subject Matter Expert), vendors, users and other stakeholders for open and pending issues to develop specifications.
• Worked on the thorough Metadata and Data analysis to make sure that all the sensitive data has been identified.
• Conducted workflow, process diagram and gap analyses to derive requirements for existing systems enhancements.
• Worked with the reference data to analyze the sensitivity of the data and secured it to protect the privacy of the client.
• Worked on developing the tool to extract the data from DB2 database and conducted Metadata and Data analysis.
• Wrote the SQL queries on data staging tables and data warehouse tables to validate the data results. .
• Used regular expressions, text parsing, and data mining techniques to search for defined data patterns.
• Created SQL-Loader scripts to load legacy data into Oracle staging tables and wrote SQL queries to perform Data Validation and Data Integrity testing.
• Created Data Dictionary and ER diagrams for data mapping purposes.
• Worked on Metadata Repository (MRM) for maintaining the definitions and mapping rules up to mark.
• Used Forward Engineering and Reverse Engineering on the existing Data Models and Updates the Data models.
• Involved in extensive DATA validation by writing several complex SQL queries and Involved in back-end testing and worked with data quality issues.
• Developed data mapping documents between Legacy, Production, and User Interface Systems.
• Use of data transformation tools such as DTS, SSIS, Informatica or Data Stage.
• Developed Python scripts to identify issues and further improve and pull new data metrics.
• Developed business requirement specification documents as well as high-level project plan.
• Maintained/updated system data flow chart, Heat Maps, Tree Maps, Visio documents, and system documentations.
• Created and maintained several custom reports using Business Objects.
• Researched and fixed data issues pointed out by QA team during regression tests.

Environment: Erwin 9.0, DB2, SSIS, Python, Oracle11g, Sql, SDLC, MS Excel 2007, MS Visio