SUMMARY:

• Over 9 years of experience in all aspects of data science: data design, data mining, statistical modeling, predictive analytics, hypothesis testing and analysis in consumer behavior, product development and product pricing wif a focus on retail and manufacturing industry
• Managed an off - site team and on-site client interface in requirement gathering, resource optimization & planning, what-if analysis and forecasting analysis capabilities and established statistical analysis methods, such as classification, association rules, time-series analysis: ARIMA, regression, statistical inference, and validation
• Built KPIs for easy monitoring of teh status of product progress and worked wif ensemble methods like boosting, bagging, linear and logistic regression and random forest for building models and machine learning
• Highly skilled in analysis of large data sets (both structured and unstructured) - data cleansing, data munging, cluster analysis, and Markov Chain Monte Carlo methods and doing design of experiments (DOE) and ANOVA wif multiple variables for validating models
• PMP certified professional wif strong communication skills and drove projects to completion by both taking ownership of projects and developing expertise in new areas

TECHNICAL SKILLS:

• Data/Statistical packages: SAS, Python, AsterR, Excel(VBA), Spark, Map Reduce, Pentaho Kettle
• Software Packages: Tableau, TIBCO Spotfire, Matlab, JMP, Minitab
• Tools: Agile, Jira, Sharepoint, Excel, ETL, PUTTY, Eclipse
• Languages: SQL, Teradata SQL, Hive, PIG, C, Pascal, VB, JavaScript
• Databases: Teradata, SQL Server, Aster, MySQL
• OS platforms: Windows, Unix, Solaris, Linux

PROFESSIONAL EXPERIENCE:

Confidential, Chicago, IL

Technical Architect

Responsibilities:

• Managed a three member team and ran scrum meetings to evaluate multiple PoCs and successfully leading to teh implementation of analytics platform for customer segmentation, which was done for teh very first time in Confidential ’s history
• Developed a Google Analytics(GA) KPIs dashboard for reviewing wif teh respective leads to draw insights on mobile app usage and drop offs in teh engagement
• Created data driven insights to segment and classify customers (clustering and multiple regression) for targeted lists
• Used global digital needs to gather requirements, evaluate scope, determine business rules, generate use cases, create process flow diagrams, and deliver final functional requirements specification documents.
• Done market basket analysis using apriori and frequency distributions to identify business rules for product bundle meals in R
• Developed time series ARIMA models for net sales and guest count for restaurants over a daily, weekly, monthly predictions to bring teh MAPE wifin 5%
• Performed A/B testing for teh mobile offers redemption based on customer segments and teh time of offers extended and user redemption rate has been improved by 8%
• Established implementation plans by collaborating wif BI Architects, Technology infrastructure architects, Data warehouse architects and project managers and reviewed teh project scope, design flow and timelines

Confidential, Dallas, TX

Data Scientist

Responsibilities:

• Developed statistical time-series models for predicting teh product price over a period of 3 years using different statistical models such as GLM, MA and ARIMA
• Worked wif BI team to implement an analytics dashboard for easy monitoring of pricing for different products
• Managed teh design, build and launch of new data models, extraction and transformation in production using SAS and R
• Developed predictive analytics, visualization and simulation modeling capabilities from large amounts of structured and unstructured data using SAS
• Collaborated wif multi-disciplinary teams to get teh data structure and analysis and confidence intervals for predicted product pricing

Confidential, San Jose, CA

Staff Engineer

Responsibilities:

• Worked cross-functionally in agile development cycle wif various teams: Product Management, Project Management, Data Architects, Data Scientists, Data Analysts, Software Engineers, and other Data Engineers in resource optimization and running test case scenarios
• Led teh design and collaborated wif team to enhance compute resource optimization & planning, what- if analysis and forecasting analysis capabilities and reporting based on manufacturing data
• Done principal component analysis (PCA), feature engineering and developed skills in analysis of large data sets in Data cleansing, cluster analysis, model fitting and Markov Chain Monte Carlo methods and doing ANOVA for multiple variables
• Developed and validated analytical and computational models to study correlation and hypothesis testing of teh data using R and SAS
• Analyzed teh text description on teh product defects using python tool in quantifying and classifying teh defects
• Conceptualized DOEs to study teh effects of teh parameters and identified teh critical parameters by principal component method using R and SAS tools and worked wif ensemble methods like boosting, bagging and random forest for building models

Senior Engineer

Responsibilities:

• Done regression fitting and extracting parameters to develop predictive models, trend analysis and simulation modeling capabilities using production data
• Performed and established statistical analysis methods, such as classification, association rules, time- series analysis, linear and logistic regression, statistical inference, A/B testing and validation methods for high volume relational data bases in production and reporting KPIs
• Responsible for identifying teh parameters dat influence teh outcome of teh performance teh product
• Developed data analysis scripts and dashboards in Spotfire and published them in html format for easy readability
• Applied statistical methods to qualify teh tools test data using t-test and established teh qualification method
• Measured and monitored repeatability and reproducibility of data collection from teh tools and validated teh statistical techniques and methodologies
• Documented teh procedural process of developing teh model and factors to be identified and monitored for bettering teh model
• Provided recommendation of teh critical independent variables dat influence teh outcome of teh dependent variable

Engineer

Responsibilities:

• Supported and maintained all teh existing data-related functionality including reports and dashboards
• Performed ETL to streamline and create a flat hierarchy for generating continuous update of teh status on dashboards.
• Developed an excel tool using VB for ranking and correlating teh factors dat has high impact on teh key performance indicators.
• Performed Monte Carlo simulations analysis and designed & implemented test cases and methodologies collaborating wif Quality assurance team