SUMMARY

• Around 8 years of hands on experience and comprehensive industry noledge of Machine Learning, Statistical Modeling, Deep Learning,DataAnalytics,Data Modeling, Data Architecture, Data Analysis, DataMining, Text Mining & Natural Language Processing (NLP), Artificial Intelligence algorithms, Business Intelligence.
• Having good experience in Analytics Models like Decision Trees, Linear & Logistic Regression, Hadoop (Hive, PIG), R, Python, Spark, Scala, MS Excel, SQL and PostgreSQL,Erwin.
• Strong noledge in all phases of the SDLC (Software Development Life Cycle) from analysis, design, development, testing, implementation and maintenance.
• Experienced in Data Modeling techniques employing Data Warehousing concepts like star/snowflake schema and Extended Star.
• Expertise in applyingDataMining techniques and optimization techniques in B2B and B2C industries.
• Expertise in writing functional specifications, translating business requirements to technical specifications, created/maintained/modified database design document with detailed description of logical entities and physical tables.
• Excellent noledge of Machine Learning, Mathematical Modeling and Operations Research. Comfortable with R, Python, SAS and Weka, MATLAB, Relational databases. Deep understanding & exposure of BigDataEco - system.
• Expertise inDataAnalysis,DataMigration,Data Profiling, DataCleansing, Transformation, Integration, DataImport, andDataExport through the use of multiple ETL tools such as Informatica Power Center.
• Proficient in Machine Learning, Data/Text Mining, Statistical Analysis&Predictive Modeling.
• Good Knowledge and experience in deep learning algorithms such as Artificial Neural network (ANN), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), LSTM and RNN based speech recognition using Tensor Flow.
• Excellent noledge and experience in OLTP/OLAP System Study with focus on Oracle Hyperion Suite of technology, developing Database Schemas like Star schema and Snowflake schema (Fact Tables, Dimension Tables) used in relational, dimensional and multidimensional modeling, physical and logical Data Modeling using Erwin tool.
• Used Cognitive Science in Machine Learning for Neurofeedback training which is essential for intentional control of brain rhythms.
• Experienced in building data models using machine learning techniques for Classification, Regression, Clustering and Associative mining.
• Good Knowledge on Natural Language Processing (NLP) and Time Series Analysis and Forecasting using ARIMA model in Python and R.
• Enabling rapid insights generation from adverse event Data via cognitive technology to increase the translational research capabilities
• Working experience in Hadoop ecosystem and Apache Spark framework such as HDFS, MapReduce, HiveQL, SparkSQL, PySpark.
• Very good experience and noledge in provisioning virtual clusters under AWS cloud which includes services like EC2, S3, and EMR.
• Proficient in data visualization tools such as Tableau, Python Matplotlib, R Shiny to create visually powerful and actionable interactive reports and dashboards.
• Expertise in building, publishing customized interactive reports and dashboards with customized parameters and user-filters using Tableau(9.x/10.x).
• Experienced in Agile methodology and SCRUM process.
• Strong business sense and abilities to communicate data insights to both technical and nontechnical clients.
• Proficient in Python, experience building, and product ionizing end-to-end systems.
• Strong programming expertise (preferably in Python) and strong in Database SQL.
• Solid coding and engineering skills preferably in Machine Learning.
• Exposure to python and python packages.
• Be a valued contributor in shaping the future of our products and services.

TECHNICAL SKILLS

Databases: MySQL, PostgreSQL, Oracle, HBase, Amazon Redshift, MS SQL Server 2016/2014/2012/2008 R2/2008, Teradata

Statistical Methods: Hypothetical Testing, ANOVA, Time Series, Confidence Intervals, Bayes Law, Principal Component Analysis (PCA), Dimensionality Reduction, Cross-Validation, Auto-correlation

Machine Learning: Regression analysis, Bayesian Method, Decision Tree, Random Forests, Support Vector Machine, Neural Network, Sentiment Analysis, K-Means Clustering, KNN and Ensemble Method

Hadoop Ecosystem: Hadoop 2.x, Spark 2.x, MapReduce, Hive, HDFS, Sqoop, Flume

Reporting Tools: Tableau Suite of Tools 10.x, 9.x, 8.x which includes Desktop, Server and Online, Server Reporting Services(SSRS)

Languages: Python (2.x/3.x), R, SAS, SQL, T-SQL

Operating Systems: PowerShell, UNIX/UNIX Shell Scripting, Linux and Windows

Data Analytics Tools: Python (numpy, scipy, pandas, Gensim, Keras), R (Caret, Weka, ggplot).

Data Visualization: Tableau, Visualization packages, Matplotlib, Seaborn, ggplot2, Microsoft Office.

R Package: dplyr, sqldf, data table, Random Forest, gbm, caret, elastic net and all sortof Machine Learning Packages.

PROFESSIONAL EXPERIENCE

Sr. Data scientist/Machine learning Engineer

Confidential, Minneapolis, MN

Responsibilities:

• Utilized Spark, Scala, Hadoop, HBase, Kafka, Spark Streaming, MLlib, R a broad variety of machine learning methods including classifications, regressions, dimensionally reduction etc. and Utilized the engine to increase user lifetime by 45% and triple user conversations for target categories.
• Participated in features engineering such as feature intersection generating, feature normalize and label encoding with Scikit-learn pre-processing.
• Performing statistical analysis on textual data. Building Machine learning/ Deep Learning models in the domain of Natural Language
• Used Python 3.X (numpy, scipy, pandas, scikit-learn, seaborn) and Spark2.0 (PySpark, MLlib) to develop variety of models and algorithms for analytic purposes.
• Application of various Machine Learning algorithms and statistical modeling like decision trees, regression models, neural networks, SVM, clustering to identify Volume using the scikit-learn package in python, Matlab.
• Create and build Dockers images for prototype deep learning models running on local GPU.
• Created Data Quality Scripts using SQL and Hive to validate successful data load and quality of the data. Created various types of data visualizations using Python and Tableau.
• Developed and implemented predictive models using machine learning algorithms such as linear regression, classification, multivariate regression, Naive Bayes, RandomForest, K-meansclustering, KNN, PCA and regularization for Data Analysis.
• Performed Data Collection, Data Cleaning, Data Visualization and developing Machine Learning Algorithms by using several packages: Numpy, Pandas, Scikit-learn and Matplotlib.
• Implemented various data pre-processing techniques to manipulate the unstructured, structured data and imbalanced data like SMOTE.
• Clustered customers' actions data by using K-means clustering and Hierarchical clustering, tan segmented them into different groups for further analyses.
• Built Support Vector Machine algorithms for detecting the fraud and dishonest behaviors of customers by using several packages: Scikit-learn, Numpy, Pandas in Python.
• Designed and developed NLP models for sentiment analysis.
• Led discussions with users to gather business processes requirements and data requirements to develop a variety of Conceptual, Logical and Physical Data Models. Expert in BusinessIntelligence and Data Visualization tools: Tableau, Microstrategy.
• Developed and evangelized best practices for statistical analysis of Big Data.
• Designed and implemented system architecture for Amazon EC2 based cloud-hosted solution for client.
• Developed deep learning algorithm dat generated hedging strategies providing 15% ROI per month with a standard deviation of 2.7%(results based on testing strategies on real data for 3 months)
• Designed the Enterprise Conceptual, Logical, and Physical Data Model for ‘Bulk Data StorageSystem ‘using Embarcadero ER Studio, the data models were designed in 3NF.
• Worked on machine learning on large size data using Spark and MapReduce.
• Collaborated with data engineers and operation team to implement ETL process, wrote and optimized SQL queries to perform data extraction to fit the analytical requirements.
• Performed data analysis by using Hive to retrieve the data from Hadoop cluster, SQL to retrieve data from RedShift.
• Explored and analyzed the customer specific features by using SparkSQL.
• Performed data imputation using Scikit-learn package in Python.
• Let the implementation of new statistical algorithms and operators on Hadoop and SQL platforms and utilized optimizations techniques, linear regressions, K-means clustering, Native Bayes and other approaches.
• Knowledge of Information Extraction, NLP algorithms coupled with Deep Learning.
• Developed Spark/Scala, SAS and R programs for regular expression (regex) project in the Hadoop/Hive environment with Linux/Windows for big data resources.
• Conducted analysis on assessing customer consuming behaviours and discover value of customers with RMF analysis; applied customer segmentation with clustering algorithms such as K-Means Clustering and Hierarchical Clustering.
• Implement deep learning algorithms to identify fraudulent transactions
• Built regression models include: Lasso, Ridge, SVR and XGboost to predict Customer Life Time Value.
• Built classification models include: Logistic Regression, SVM, Decision Tree, RandomForest to predict Customer Churn Rate.
• Used F-Score, AUC/ROC, Confusion Matrix, MAE, RMSE to evaluate different Model performance.

Environment: AWS RedShift, EC2, EMR, Hadoop Framework, S3,HDFS, Spark(Pyspark, MLlib, Spark SQL), Python 3.x (Scikit-Learn/Scipy/Numpy/Pandas/Matplotlib/Seaborn),Tableau Desktop (9.x/10.x), Tableau Server (9.x/10.x), Machine Learning (Regressions, KNN, SVM, Decision Tree, Random Forest, XGboost, LightGBM, Collaborative filtering, Ensemble),Deep Learning, Teradata, Git 2.x, Agile/SCRUM

Data scientist/Machine learning Engineer

Confidential, Raritan, NJ

Responsibilities:

• Tackled highly imbalanced Fraud dataset using undersampling, oversampling with SMOTE and cost sensitive algorithms with Python Scikit-learn.
• Wrote complex Spark SQL queries for data analysis to meet business requirement.
• Developed MapReduce/Spark Python modules for predictive analytics & machine learning in Hadoop on AWS.
• Building Optimization models using Machine Learning, Deep Learning algorithms.
• Worked on data cleaning and ensured Data Quality, consistency, integrity using Pandas, Numpy.
• Participated in feature engineering such as feature intersection generating, feature normalize and label encoding with Scikit-learn preprocessing.
• Improved fraud prediction performance by using random forest and gradient boosting for feature selection with Python Scikit-learn.
• Performed feature engineering, performed NLP by using some techniques like Word2Vec, BOW (Bag of Words), Tf-Idf, Word2Vec, Doc2Vec.
• Performed Naïve Bayes, KNN, Logistic Regression, Random Forest, SVM and XG boost to identify whether a loan will default or not.
• Implemented Ensemble of Ridge, Lasso Regression and XGboost to predict the potential loan default loss.
• Used various Metrics (RMSE, MAE, F-Score, ROC and AUC) to evaluate the performance of each model.
• Performed data cleaning and feature selection using MLlib package in PySpark and working with deep learning frameworks.
• Actively involved in all phases of data science project life cycle including Data Extraction, Data Cleaning, Data Visualization and building Models.
• Experience in working with languages Python and R.
• Developed text mining models using Tensor Flow & NLP (NLTK, SpaCy and CoreNLP) on call transactions & social media interaction data for existing customer management.
• Experienced in Agile methodology and SCRUM process.
• Experience in Extract, Transfer and Load process using ETL tools like Data Stage, Data Integrator and SSIS for Data migration and Data Warehousing projects.
• Experienced in Data Integration Validation and Data Quality controls for ETL process and Data Warehousing using MS Visual Studio, SSAS, SSIS and SSRS.
• Used big data tools Spark (Pyspark, SparkSQL and MLlib) to conduct Realtime analysis of loan default based on AWS.

Environment: MS SQL Server 2014, Teradata, ETL, SSIS, Alteryx, Tableau (Desktop 9.x/Server 9.x), Python3.x (Scikit-Learn/Scipy/Numpy/Pandas), Machine Learning (Naïve Bayes, KNN, Regressions, Random Forest, SVM, XGboost, Ensemble), AWS Redshift, Deep Learning, Spark (PySpark, MLlib, Spark SQL), Hadoop 2.x, Map Reduce, HDFS, SharePoint.

Data Scientist

Confidential, Austin, TX

Responsibilities:

• Gathered, analyzed, documented and translated application requirements into data models and Supports standardization of documentation and the adoption of standards and practices related to data and applications.
• Participated in Data Acquisition with Data Engineer team to extract historical and real-time data by using Sqoop, Pig, Flume, Hive, MapReduce and HDFS.
• Automated csv to chatbot friendly Json transformation by writing NLP scripts to minimize development time by 20%.
• Wrote user defined functions (UDFs) in Hive to manipulate strings, dates and other data.
• Performed Data Cleaning, features scaling, features engineering using pandas and numpy packages in python.
• Applied clustering algorithms i.e. Hierarchical, K-means usingScikit and Scipy.
• Created logical data model from the conceptual model and it's conversion into the physical database design using ERWIN.
• Mapped business needs/requirements to subject area model and to logical enterprise model.
• Worked with DBA's to create a best fit physical data model from the logical data model
• Redefined many attributes and relationships in the reverse engineered model and cleansed unwanted tables/ columns as part of data analysis responsibilities.
• Enforced referential integrity in the OLTP Data Model for consistent relationship between tables and efficient database design.
• Developed the data warehouse model (star schema) for the proposed central model for the project.
• Created 3NF business area data modeling with de-normalized physical implementation data and information requirements analysis using ERWIN tool.
• Worked on the Snow-flaking the Dimensions to remove redundancy.
• Worked in using Teradata14 tools like Fast Load, Multi Load, T Pump, Fast Export, Teradata Parallel Transporter (TPT) and BTEQ.
• Helped in migration and conversion of data from the Sybase database into Oracle database, preparing mapping documents and developing partial SQL scripts as required.
• Generated ad-hoc SQL queries using joins, database connections and transformation rules to fetch data from legacy Oracle and SQL Server database systems.

Environment: Machine learning(KNN, Clustering, Regressions, Random Forest, SVM,Ensemble), Linux, Python 2.x (Scikit-Learn/Scipy/Numpy/Pandas), R, Tableau (Desktop 8.x/Server 8.x), Hadoop, Map Reduce,HDFS, Hive, Pig, HBase,Sqoop, Flume,Oracle 11g, SQL Server 2012.

BI Developer/Data Analyst

Confidential, New York City, NY

Responsibilities:

• Used SSIS to create ETL packages to Validate, Extract, Transform and Load data into Data Warehouse and Data Mart.
• Maintained and developed complex SQL queries, stored procedures, views, functions and reports dat meet customer requirements using Microsoft SQL Server 2008 R2.
• Created Views and Table-valued Functions, Common Table Expression (CTE), joins, complex subqueries to provide the reporting solutions.
• Optimized the performance of queries with modification in T-SQL queries, removed the unnecessary columns and redundant data, normalized tables, established joins and created index.
• Created SSIS packages using Pivot Transformation, Fuzzy Lookup, Derived Columns, ConditionSplit, Aggregate, Execute SQL Task, Data Flow Task and Execute Package Task.
• Migrated data from SAS environment to SQL Server 2008 via SQL Integration Services (SSIS).
• Developed and implemented several types of Financial Reports (Income Statement, Profit& Loss Statement, EBIT, ROIC Reports) by using SSRS.
• Collaborated with database engineers to implement ETL process, wrote and optimized SQL queries to perform data extraction and merging from SQL server database.
• Created Complex ETL Packages using SSIS to extract data from staging tables to partitioned tables with incremental load.
• Gathered, analyzed, and translated business requirements, communicated with other departments to collected client business requirements and access available data.
• Migrating data from Legacy system to SQL Server using SQL Server Integration Services 2012.
• Used C# scripts to map records.
• Involved in writing complex SQL Queries, Stored Procedures, Triggers, Views, Cursors, Joins, Constraints, DDL, DML and User Defined Functions to implement the business logic and created clustered and non-clustered indexes.
• Created and modified Stored Procedures, Functions, and Indexes.
• Developed SQL Scripts to Insert/Update and Delete data in MS SQL database tables.
• Created various ad-hoc SQL queries for customer reports, executive management reports and types of report types like tables, matrix, sub reports etc.
• Designed and developed new reports and maintained existing reports using Microsoft SQLReporting Services (SSRS) and Microsoft Excel to support the firm's strategy and management.
• Created sub-reports, drill down reports, summary reports, parameterized reports, and ad-hoc reports using SSRS.
• Used SAS/SQL to pull data out from databases and aggregate to provide detailed reporting based on the user requirements.
• Used SAS for pre-processing data, SQL queries, Data Analysis, generating reports, Graphics, and Statistical analyses.
• Provided statistical research analyses and Data Modeling support for mortgage product.
• Perform analyses such as regression analysis, logistic regression, discriminant analysis, cluster analysisusing SAS programming.

Environment: SQL Server 2008 R2, DB2, Oracle, SQL Server Management Studio, SAS/ BASE, SAS/SQL, SAS/Enterprise Guide, MS BI Suite(SSIS/SSRS), T-SQL, SharePoint 2010, Visual Studio 2010, Agile/SCRUM

Data Analyst

Confidential

Responsibilities:

• Wrote SQL queries for data validation on the backend systems and used various tools like TOAD&DB Visualizer for DBMS (Oracle).
• Perform Data analysis, Backend Database testing, Data Modeling and Developing SQL Queries to solve problems and meet user's need for Database management in Data Warehouse.
• Utilize object-oriented languages, concepts, database design, star schemas and databases.
• Create algorithms as needed to manage and implement proposed solutions.
• Participate in test planning and test execution for functional, system, integration, regression, UAT (User Acceptance Testing), load and performance testing.
• Work with test automation tools for recording/coding in Database, and execute in regression testing cycles.
• Transferred data from various OLTP data sources, such as Oracle, MS Access, MS Excel, Flat files, CSV files into SQL Server.
• Working with Databases DB2, Oracle DM, SQL Server for Database testing and maintenance.
• Involved in writing and executing User Acceptance Testing (UAT) with end users.
• Involved in Post- Implementation validations after the changes have been to the Data Marts.
• Chart out Graphs, and Reports alike in QC to point out the percentage of Test Cases passed, and thereby to point out the percentage of Quality achieved and uploading the status daily to ART reports an in-house tool.
• Performed extensive Data Validation,Data Verification against Data Warehouse.
• Used UNIX to check the Data marts, Tables and Updates made to the tables.
• Writing advanced SQL Queries to query the data from Data marts and Landings to verify the changes has been made.
• Involved in Client requirement gathering, participated in discussion & brain storming sessions and documented requirements.
• Validating and profilingFlat FileDatainto Teradata tables using UNIX Shell scripts.
• Actively participated Functional, System and User Acceptance testing on all builds and supervised releases to ensure system / functionality integrity.
• Closely interacted with designers and software developers to understand application functionality and navigational flow and keep them updated about Business user sentiments.
• Interacted with developers to resolve different Quality Related Issues.
• Wrote and executed manual test cases for functional, GUI, and regression testing of the application to make sure dat new enhancements do not break working features
• Writing and executing Manual test cases in HP Quality Center.
• Wrote test plans for positive and negative scenarios for GUI and functional testing
• Involved in writing SQL queries and stored procedures using Query Analyzer and matched the results retrieved from the batch log files
• Created Project Charter documents & Detailed Requirement document and reviewed with Development & other stake holders.

Environment: Subversion, TortoiseSVN, Jira, Agile-Scrum, Web Services, Mainframe, Oracle, Perl, UNIX, LINUX, Shell Scripts, UML, Quality Center, RequisitePro, SQL, MS Visio, MS Project, Excel, Power Point, Word, SharePoint, Win XP/7 Enterprise.

Data Analyst/Data Modeler

Confidential

Responsibilities:

• Data analysis and reporting using MY SQL, MS Power Point, MS Access and SQL assistant.
• Involved in MY SQL, MS Power Point, MS Access Database design and design new database on Netezza which will have optimized outcome.
• Used DB2 Adapters to integrate between Oracle database and Microsoft SQL database in order to transfer data.
• Designed the data marts using the Ralph Kimball's Dimensional Data Mart modeling methodology using ER Studio.
• Involved in writing T-SQL, working on SSIS, SSRS, SSAS, Data Cleansing, Data Scrubbing and Data Migration.
• Used Normalization methods up to 3NF and De-normalization techniques for effective performance in OLTP systems.
• Initiated and conducted JAD sessions inviting various teams to finalize the required data fields and their formats.
• Involved in designing and implementing the Data Extraction (XML DATA stream) procedures.
• Created base tables, views, and index. Built a complex Oracle procedure in PL/SQL for extract, loading, transforming the data into the warehouse via DBMS Scheduler from the internal data.
• Involved in writing scripts for loading data to target data Warehouse using BTEQ, Fast Load, MultiLoad.
• Create ETL scripts using Regular Expressions and custom tools (Informatica, Pentaho, and Sync Sort) to ETL data.
• Developed SQLService Broker to flow and sync of data from MS-I to Microsoft's master database management (MDM).
• Extensively involved in Recovery process for capturing the incremental changes in the source systems for updating in the staging area and data warehouse respectively
• Strong noledge of Entity-Relationship concept, Facts and dimensions tables, slowly changing dimensions and Dimensional Modeling (Star Schema and Snow Flake Schema).
• Involved in loading data between Netezza tables using NZSQL utility.
• Worked on Data modeling using Dimensional Data Modeling, Star Schema/Snow Flake schema, and Fact & Dimensional, Physical & Logical data modeling.
• Generated Stats pack/AWR reports from Oracle database and analyzed the reports for Oracle8.x wait events, time consuming SQL queries, table space growth, and database growth.

Environment: ER Studio, MY SQL, MS Power Point, MS Access, MY SQL, MS Power Point, MS Access, Netezza, DB2, T-SQL, DTS, Informatica MDM, SSIS, SSRS, SSAS, ETL, MDM, 3NF and De-normalization, Teradata, Oracle8.x, (Star Schema and Snow Flake Schema) etc.