Data Scientist
Current Worked closely with business, data governance, SMEs and vendors to define data requirements. Used R, SQL to create Statistical algorithms involving Multivariate Regression, Linear Regression, Logistic Regression, PCA, Random forest models, Decision trees, Support Vector Machine for estimating the risks of welfare dependency. Interpret problems and provides solutions to business problems using data analysis, data mining, optimization tools, and machine learning techniques and statistics. Actively participated in data modeling, data warehousing and complex database designing. Designed and developed NLP models for sentiment analysis. Worked in using Teradata14 tools like Fast Load, Multi Load, TPump, Fast Export, Teradata Parallel Transporter (TPT) and BTEQ. Participated in all phases of data mining; data collection, data cleaning, developing models, validation, visualization and performed Gap analysis. Involved in creating Data Lake by extracting customer's Big Data from various data sources into HadoopHDFS. This included data from Excel, Flat Files, Oracle, SQL Server, MongoDb, Cassandra, HBase, Teradata, Netezza and also log data from servers Created high level ETL design document and assisted ETL developers in the detail design and development of ETL maps using Informatica. Adept at using SAS Enterprise suite, Python, and BigData related technologies including knowledge in Hadoop, Hive, Sqoop, Oozie, Flume, Map-Reduce Worked on predictive and what-if analysis using R from HDFS and successfully loaded files to HDFS from Teradata, and loaded from HDFS to HIVE. Analyzed data and predicted end customer behaviors and product performance by applying machine learning algorithms using SparkMLlib. Performed data mining on data using very complex SQL queries and discovered pattern and used extensive SQL for data profiling/analysis to provide guidance in building the data model.
