SUMMARY:

• SAS Programmer/Hadoop Developer with 8 years of experience in Analysis, Design, Development, Testing and Implementation of Statistical models and applications in Windows environment.
• Proficiency with Hadoop , MapReduce.
• Experience in building and operationalizing Hadoop based data lake
• Experience building ETL workflow to transform, aggregate and build business metrics using Apache Spark,
• Big Data querying tools, such as Pig, Hive, and Drill
• Experience in Development, Testing and Administration of Big Data and Web Application Projects
• Hands on Experience in writing Map - Reduce programs in JAVA
• Hands on Experience in batch-scheduling using Apache OOZIE;
• Experience developing Data Lake and doing ETL using tools like KAFKA, FLUME, SQOOP and Camus
• Proficiency in Object Oriented Programming (OOP)
• Hands on experience with various Hadoop Distributions - Cloudera, Hortonworks and Apache
• Hands on experience in Lambda architecture for designing big data systems
• Experience in Performance and Load testing of RESTful APIs - nGrinder and GATLING
• Expertise with segmentation modeling, & profiling, principal component analysis, boosting and bagging, decision trees, correlation, regression, time series models, conjoint analysis, survival analysis, factor analysis and profitability models .
• Hands-on experience of using SAS/BASE, SAS/STAT, SAS/GRAPHS, SAS/Macros,SAS/ACCESS, SAS/SQL, SAS/ETS and Arrays in windows environments.
• Experience in using SAS to import/export data to external file formats like flat, Excel, XML files
• Thorough knowledge in SAS Programming, using Base SAS, Macro Facility, Proc SQL, SAS Procedures, SAS Functions, SAS Formats, and ODS facility in data scrubbing, manipulation, and preparation to produce summary datasets and reports.
• Extensively used merge Procedures like PROC APPEND, PROC DATASETS, PROC SORT, PROC TRANSPOSE.
• Extensive experience in generating Reports employing various SAS data steps and proc Steps such as DATA NULL, PROC REPORT, PROC TRANSPOSE, PROC FORMAT, PROC MEANS, PROC FREQ, PROC TABULATE, etc.
• Demonstrated knowledge in the use of advanced data look-up techniques such as array processing, hash objects, formats, and combining/merging data.
• Built custom quantitative models by applying custom quantitative methods to drive Descriptive, Predictive and prescriptive analytics through predictive modeling, forecasting, data mining, simulation and /or optimization
• Performing effective benchmarking by using the appropriate SAS System options and interpreting the resulting resource utilization statistics.
• Possess strong ability to understand and stay in front of emerging trends in applied Advanced Analytics, predictive modeling and Data Science and utilize this knowledge to solve business issues ingeniously enabling fact - based decision making .
• Advanced knowledge of industry data sources and the ability to work with large databases and datasets for extraction, conversion into useful business information, and analytical purposes
• Advanced expertise in excel (Macros and pivot Tables) and PowerPoint.
• Highly motivated individual with excellent organizational and interpersonal skills.
• Possess strong ability to quickly adapt to new applications and platforms.

TECHNICAL SKILLS:

Big Data Hadoop: HDFS, Map reduce, Pig, Hive, Flume, Kafka, Oozie, ScoopSAS Tools: SAS V9.x, Base SAS, SAS/SQL, SAS/MACROS, SAS/STAT, SAS/ACCESS, SAS/ GRAPH, SAS/ODS, ETL, SAS BI Server and SAS Data Integration Platform 4.2, SAS Forecast Studio, SAS Enterprise Guide, SPSS, SAS EBI, Predictive Analytics, Data Mining

Operating Systems: Windows2000/XP, UNIX, Linux

Databases: Oracle 9i

Programming Languages: SQL, Java

Office Tools: MS-OFFICE, Word, Excel, Access, PowerPoint

Other: Google Analytics, R, R-studio, Big Data, Hadoop, map reduce.

PROFESSIONAL EXPERIENCE:

Hadoop Big Data Analyst

Confidential

Responsibilities:

• Using data visualizations, identifying patterns using moving averages, distribution histograms, standard deviations and clustering to prioritize data collection and analysis.
• Identifying core determinants of process performance using correlation analysis and forming an initial hypothesis about root causes of yield drop and variability.
• With the help of significance testing, testing the initial hypothesis of root causes of yield drop and variability and focus on the most statistically significant factors for further investigation.
• Using predictive analytics reducing the number of tests required for quality assurance. Starting at the wafer level, analyzing data from the manufacturing process to cut down test time and focus on specific tests.
• Forecasting of product demand and production, analyzing plant performance across multiple metrics and providing service and support to customers faster
• Addressing quality and performance issues before they escalate. Drive continuously improved reliability, efficiency and quality by quickly combining and analyzing huge quantities of data using predictive modeling and analytics. Enabling more uptime and smoother operations by using root-cause analysis to troubleshoot and correct issues fast.
• Gaining insight into real and perceived quality issues. Access and analyze all types of data, whether it's from call center systems or written records of service calls. Then integrate the data with issue detection process for earlier warnings and corrective action guidance.
• Prioritizing warranty work effectively and slashing contact center costs. Pulling together warranty data from multiple sources into a single database, and quickly decode its meaning.
• Enabling and supporting quality-driven strategy. Working quickly to address issues. Then linking customer feedback and expectations with design, engineering, manufacturing and packaging.
• Analyzing the behavior of repeat customers there by understanding how to deliver goods in a timely and profitable manner.

Data Analyst

Confidential

Responsibilities:

• Ingesting data to Hadoop from variety of sources like ERP, CRM and transactional systems .
• Building Hadoop based ETL workflow to transform and aggregate data .
• Building a multi-tenant bigdata platform with data security and access control .
• Monitoring performance and advising any necessary infrastructure changes.
• B uilding stream-processing systems, using solutions such as Storm or Spark-Streaming or MapR stream
• Collecting, storing, processing, and analyzing of huge sets of data in MapR Hadoop ecosystem.
• Building and operationalizing Hadoop based data lake and different analytics layers for data science and reporting within Hadoop.
• Solved and optimized the Cluster Environment issues in Hadoop.
• Developed excellent quality code while aligning with techniques such as TDD

Environment: Big Data, Hadoop,SAS/Base, SAS/STAT, SAS/SQL, SAS/Macro, SAS/Access, SAS ODS, SAS/GRAPH, SAS/IML, SAS/ ETL, SAS BI, SAS Views, SAS Enterprise Guide, SAS/information mapping, Excel, Windows XP, Google Analytics

Confidential, San Francisco

Marketing Analyst

Responsibilities:

• Development and analysis of reports for the Marketing department and other SBU’s needs, including regular competitive studies showing market place position and disconnection; potential market opportunities, both demographically and geographically.
• Apply Database Relationship Management, profile customers, influence the design of direct marketing or business initiatives; evaluate effectiveness of marketing campaigns and agency performance.
• Run ad-hoc data queries, analyze statistical data including retention, attrition rates and loss ratios by various segments (e.g. company, States)
• Analysis included Behavioral model, Customer Profiling, Segmentation, Trend Analysis, Predictive Modeling,
• Used the SAS Forecast Studio for appropriate analysis of data for different Forecasting models like Time Series Forecasting.
• Used various Statistical Procedures like PROC FREQ, PROC REG, PROC ANOVA and PROC GLM to analyze data and generate reports.
• Imported raw data files in excel format in SAS and subsequently created SAS Datasets and performed data manipulations on the datasets.
• Cleaned existing data and converted them into useful SAS Datasets, merged datasets and created reports based on Ad-hoc requirements.
• Program efficiently and accurately in SAS to mine, pull, and evaluate data from all various data sources
• Performed SAS/SQL analysis and reporting.
• Developed new or modified SAS programs and used SQL Pass Through and Libname methods to extract data and created study specific SAS datasets, which are used as source datasets for report generating programs.
• Generated reports, tables and graphs for presentations and data modeling
• Reviewed deliverables before transfer to either internal or external clients.

Environment: SAS/Base, SAS/STAT, SAS/SQL, SAS/Macro, SAS/Access, SAS ODS, SAS/GRAPH, SAS/IML, SAS/ ETL, SAS BI, SAS Views, SAS Enterprise Guide, SAS/information mapping, Excel, Windows XP, Google Analytics

SAS Analyst

Confidential

Responsibilities:

• Data preparation, cleaning, manipulation, and visualization. Extracted data from relational databases and perform complex data manipulations. Conducted extensive data checks to ensure data quality.
• Analyze and mine multiple data sets to select statistically valid data samples, build, test, and implement predictive models by investigating appropriate methods
• Engage in quantitative analysis on many non-standard and unique business problems using decision trees, neural networks, clustering , etc to deliver actionable output.
• Created and used user-defined and automatic macro variables within the SAS Macro Language.
• Automated programs by defining and calling macros using the SAS Macro Language.
• Used various system options that are available for macro debugging and displaying values of user-defined and automatic macro variables in the SAS log.
• Performed TLG programming using SAS Procedures such as SUMMARY,MEANS,UNIVARIATE,FREQ,SORT,PRINT,TABULATE,PLOT,CHART and REPORT to compute elementary statistical measures and produce data listings, tabulations and graphical displays
• Generated output files in RTF,HTML,PDF formats using SAS ODS
• Participated in the review of Statistical Analysis Plans, QC Specifications and Database Structures.
• Designed and wrote standard maintainable, supportable, well- documented computer programs that are user-friendly and accessible.
• Performed SAS/SQL analysis and reporting
• Developed standard macros and/or tools in SAS for data analysis and reporting.

Environment: SAS/Base, SAS/STAT, SAS/SQL, SAS/Macro, SAS/Access, SAS ODS, SAS/GRAPH, SAS/IML, SAS/ ETL, SAS BI, SAS Views, SAS Enterprise Guide, SAS/information mapping, Excel, Windows XP.

Data Analyst

Confidential

Responsibilities:

• Data cleaning and preparation for Data Analysis.
• Creation of data sets using libname statements.
• Generating graphs and reports on a weekly basis to be sent to representatives for analysis.
• Creation of user defined formats and labels for easy understanding of the data.
• Importing and exporting of data files from and to excel and CSV files respectively.
• Updating the excel files on a day to day basis and checking for data Quality.
• Using tableau generating graphs and tables for presenting it to the management.