SUMMARY

• Approximately 6 years of experiences as a Data Analyst/Data Engineer/BI Developer
• Experienced in developing logical and physical data models and organizing data
• Experienced with complex business challenges and manipulating large data sets using statistical techniques
• Built dashboard using Business Intelligence (BI) tools: Tableau, Qlik View, Microsoft Power BI, and Excel
• Used R & Python, SAS, Tableau, Power BI and SQL for data cleaning, data visualization, risk analysis and predictive analytics
• Used big data technologies such as Hadoop, Hive, Pig, Sqoop and Flume for data streaming and analysis
• Done data mining with appropriate algorithm (clustering and others) on structured and semi - structured data.
• Analyzed data by reading them from Oracle, XML, DB2, Hadoop, AmazonS3, SQL Developer and others
• Worked closely with data scientists to build predictive, machine learning and deep learning algorithms
• Used machine learning (ML) tools and libraries such as Scikit-learn, R, Spark, and others
• Built data pipelines for reporting, alerting, and data mining
• Working with large-scale data set (millions of entities)
• Worked as an Agile champion (Scrum/Kanban)
• Done ETL using Pentaho and informatica
• Deep understanding on SDLC (Software Development Life Cycle)
• Done Deployment in association with Maven, Jenkins, and Git-Hub

TECHNICAL SKILLS

• R, Python, SQL, SAS Base, basic proficiency in Scala, Stata, MATLAB, Hadoop, MapReduce
• MS Azure ML, Cloudera, Spark, SAS EM, Eclipse, Erwin, IPython, SQL Server, Spring Framework, Jenkins, Maven, MySQL, Oracle, RedShift, Tableau, MS Excel, MS PowerPoint, QlikView, SAP, Microsoft Power BI and Business Objects, JIRA, TFS, RTC, Git-Hub
• Windows, Mac OS, UNIX, and LINUX.

PROFESSIONAL EXPERIENCE

Confidential - Austin, TX

Senior Data Analyst/BI Developer

Responsibilities:

• Worked closely with Confidential clients: MasterCard, FRBD (Federal Reserve Bank of Dallas), (EBT, ECC, WIC and EPC GO) to collect high level requirements and break them into technical requirements
• Analyzed use cases on sales promotion, fraudulent transaction analysis and potential customer base to form predictive models and to explore business expansion opportunity and to prevent fraud
• Worked on customer requirement analytics and improved overall customer satisfaction
• Built dashboards using Tableau and Power BI and presented before clients
• Utilized data mining and classification algorithms, in association with Data Scientists to identify hidden patterns and build categorization model, relevant to various business problems
• Reviewed suspicious activities and complex fraud cases to identify and resolve insurance fraud trends
• Analyzed data to investigate, identify and report trends linked to fraudulent transactions and claims
• Translated data into insights to drive key business decisions
• Built dashboards to identify business scopes, heatmaps of customers and fraud zones and others
• Collected, cleaned, sanitized, and analyzed data in large volume and automated for self-monitoring, self-diagnosing, self-correcting solutions and for optimizing of key processes.
• Build predictive models, in association with Data Scientists based on Regressions, Artificial Neural Networks, Random Forest, Correlation, Cluster Analysis and others
• Built scoring systems for fraud identifications using decision tree, in association with the Data Scientists
• Done coding in R, Python, SQL, and Linux
• Mined data from sources, e.g. Oracle, MS SQL Server, DB2, and Hadoop based data storage systems
• Analyzed historical data nodes and re-engineered and mapped per user requirements
• Excellent knowledge on Normalization and De-normalization techniques
• Used SQL and HQL to wrangle data and form tables on the fly
• Used R (RStudio) and Python (PySpark) for data cleaning, visualization, and risks & predictive analytics
• Mined geospatial data (GIS). Used multiple R libraries like ggmap, rgdal, gdal, rgeos,etc
• Done ETL with Informatica and Pentaho
• Performed data quality assessments based on custom quality assurance metrics
• Used JIRA as tracking tool

Confidential - Long Island, NY

Data Analyst

Responsibilities:

• Worked with Finance department to integrate algorithms and data into Return Path solutions
• Worked closely data scientists and managers to define requirements of scoring models.
• Analyzed customer data to tune rules, exposes patterns, research anomalies, reduce false positives, and build executive and project-level reports
• Investigated new technologies on the future of digital marketing
• Clearly and thoroughly documented investigation findings and conclusions
• Developed debt repayment models to predict debt repayments, owed by individuals and small businesses
• Analyzed transactions to identify promotion scopes using tools like Hadoop, Spark, HIVE and AWS suite
• Created dashboards using Tableau to communicate complex ideas to the executive committees
• Developed Logical and Physical Data models and organized data as per the business requirements
• Assisted in development of predictive models and coded in R
• Worked on pilot project to migrate models from R to Python using Anaconda IDE
• Used Hive and Sqoop to build tables and pull data from transactional data bases to NoSQL data bases (Accumulo- Hadoop Big table)
• At the same time extracted and manipulated data with SQL from Oracle based transactional data bases
• Worked as a data engineer and provided end to end support by extracting, analyzing, and interpreting data using aggregation functions, making strategic recommendations, and presenting before internal clients
• Drafted Business Intelligence level analytics on Pentaho and presented before clients
• Worked with machine learning tools and libraries such as Scikit-learn, PyTorch, Pandas, Keras
• Identified meaningful insights from chargeback data
• Done exploratory data analysis to find trends and formed clusters
• Assisted in building models using techniques like Regression (linear/logistic), Tree based ensemble methods, Time Series forecasting, KNN, Clustering, SVM and others
• Worked both with structured, semi—structured (xml) and unstructured (text) data
• Communicated and coordinated with other departments to collection business requirement
• Used RTC (Rational Team Concert by IBM) as an agile tracking tool

Confidential

BI Developer

Responsibilities:

• Built data conversion requirements (technical requirements) based on business requirements
• Built Dashboards using Tableau and Qlik View to wrangle customer data (both open source and internal) and to assist in identifying prospective customer base, newer pricing strategies and internal cost trends
• Wrote a python-based SQL generator that helped speed up a weekly reporting from several days
• Wrote SQL queries for data extraction, manipulation and formation of table
• Maintained a Python/Django web application
• Done coding in R and Python to form, test and tun predictive model
• Created test and train data sets
• Data bases used are mainly oracle based. However, I was part of a team who were testing and piloting DevOps storage-based Hadoop (local data center)
• Assisted in building data models for big data and transactional data bases (oracle based) system
• Assisted in tuning databases, which include indexes, optimizing SQL statements and monitoring server
• Collaborated to form data dictionary, data tagging workbook and data mapping documents
• Co-ordinated with various business users, stakeholders, and SMEs to get Functional expertise, design, and business test scenarios review, UAT participation and validation of financial data
• Used vision to design and develop Use Cases, Activity Diagrams, Sequence Diagrams, OOD (Object oriented Design) using UML and Visio
• Used JIRA & TFS as tracking tool