SUMMARY

• Over 5+ years of IT Experience in data base and data science, Machine Learning Algorithms, and Visualization.
• Extensive experience working in various domains like Telecom, Banking, and Automobiles.
• Experience in exploratory data analysis (EDA) using R language.
• Experience in writing code in R and Python to manipulate data for data loads, extracts, statistical analysis, and modeling.
• Experience in SAS
• Hands on Experience working on Amazon Redshift.
• Identified areas of improvement in existing business by unearthing insights by analyzing vast amount ofdatausing machine learning techniques
• Experience in using Decision Trees, k - Nearest Neighbors, Clustering (hierarchical and k-means), Genetic algorithm, Dijkstra algorithm.
• Designed and implemented statistical / predictive models utilizing diverse sources ofdatato predict demand, risk, and priceelasticity.
• Conducted in-depthanalysisand predictive modelling to uncover hidden opportunities; communicate insights to teh product, sales, and marketing teams.
• Experience in Hadoop, MapReduce, MongoDB, and HDFS
• Experience in creating different types of data visualization using R, Power BI, and Tableau.
• Experience in Installing, Upgrading and Configuring Microsoft SQL Server.
• Participated all stages in Agile Scrum methodologies of project management.
• Skilled Confidential assessing client needs, working in groups, suggesting ideas dat enhance efficiency and performance, implementing technology solutions, and training end users.

TECHNICAL SKILLS

Languages: R (4yrs), Python (4yrs), SAS (4yrs), T-SQL ( 6yrs), SQL (6yrs), HTML (2yrs), C(6yrs), C++(6yrs)

Tools: R Studio, Microsoft Azure, Enterprise Manager, MS SQL, SSRS, SSIS, SSAS, Business Intelligence Development Studio (BI), Visual Studio 2013, Oracle, MongoDB, Amazon Redshift

BigData Ecosystems: Hadoop(4yrs), HDFS, MapReduce

Reporting Tools: MS Office 2003/2007, SQL Server Reporting Services, Crystal Reports, Power BI, Tableau.

Statistical Techniques: Machine learning (3yrs), Decision Trees, k-Nearest Neighbors, Clustering (hierarchical and k-means), Genetic algorithm, Dijkstra algorithm.

PROFESSIONAL EXPERIENCE

Confidential, Bellevue, WA

Data Scientist

Responsibilities:

• dis project was focused on customer segmentation based on machine learning and statistical modeling effort including buildingpredictive models and generatedataproducts to support customer segmentation.
• Develop a pricing model for various product & services bundled offering to optimize and predict teh gross margin.
• Built priceelasticitymodel for various product and services bundled offering.
• Developed predictive causal model using annual failure rate and standard cost basis for teh new bundled service offering.
• Design and develop analytics, machine learning models, and visualizations dat drive performance and provide insights, from prototyping to production deployment and productrecommendation and allocation planning;
• Worked with sales and Marketing team for Partner and collaborate with a cross-functional team to frame and answer important data questions. prototyping and experimenting ML/DLalgorithms and integrating into production system for different business needs.
• Worked on Multiple datasets containing 2billion values which are structured and unstructured dataabout web applications usage and online customer surveys.
• Good hands on experience on Amazon Redshift platform.
• Design, built and deployed a set of python modeling APIs for customer analytics, which integrate multiplemachinelearningtechniques for various user behavior prediction and support multiple marketingsegmentation programs.
• Segmented teh customers based on demographics using K-means Clustering.
• Explored different regression and ensemble models in machine learning to perform forecasting.
• Used classification techniques including Random Forest and Logistic Regression to quantify teh likelihood of each user referring.
• Designed and implemented end-to-end systems forDataAnalytics and Automation, integrating custom visualization tools using R, Tableau, and Power BI.

Environment: MS SQL Server, R/R studio,SAS, Python, Redshift, MS Excel, Power BI, Tableau, T-SQL, ETL, MS Access, XML, MS office 2007, Outlook.

Confidential, Dallas, TX

Data Scientist

Responsibilities:

• Developed algorithm to analyze sentimentaldataand detecting trend in customer usage and other services.
• Worked on Multiple datasets containing 1billion values which are structured and unstructured data.
• Used predictive analysis to create models of customer behavior dat are correlated positively with historical data and use these models to forecast future results.
• Explored teh user API's and documented relevant API's for teh project and created a work-flowplan.
• Evaluated feature importance related to customer segmentation, preference, and prediction via Random Forest and Logistic Regression.
• Analyzed historicaldata, documentation, supporting documentation, screen prints, and email conversations.
• Implemented K-means and Clustering Algorithms to group users according to teh usage and requirements of teh services by area.
• Showed dynamic visualization of clustering of user’s coverage and network usage based on teh area by using R Studio.
• Predicted user preference based on segmentation using General Additive Models, combined with feature clustering, to understand non-linear patterns between user segmentation and related monthly platform usage features (time seriesdata).
• Conducted linear regression to predict teh transaction volume, and distinguished frequent claimers based on MapReduce using Hadoop and R studio.

Environment: R/R studio, Python, Tableau, Hadoop, MS SQL Server 2005/ 2008, MS Access, MS Excel, Outlook, Power BI.

Confidential, Oklahoma

Data Analyst

Responsibilities:

• Support teh Debt Collection Application specifically application related Payment Imports and skip tracing of teh consumer address information.
• Designed and implemented end-to-end systems forDataAnalytics and Automation, integrating custom visualization tools using R, Hadoop, MongoDB, Tableau, and Power BI.
• Responsible for creating Summary reports, Sub reports, Drill Down reports, Matrix reports.
• Developed Stored Procedures for parameterized, drill-down, and drill-through reports in SSRS.
• Formatted reports using Global Variables, Expressions, and Functions for teh reports.
• Created different graphical reports using DAX queries for better stimulation of data in Power BI.
• Created teh DTS Package through ETL Process to vendors in which records were extracts from Flat file and Excel sources and loaded daily Confidential teh server.
• Assess detailed specifications against design requirements.
• Responsibilities taken as a production support Developer for applications as on needed basis.
• Knowledge of financial instruments such as making payments to teh account after splitting them to teh accounts based on teh commission rates and teh bucket levels.

Environment: SQL Server 2000/2005 Enterprise Edition, SQL Enterprise manager, R/R studio, MS PowerPoint, MS Access 2000 & Windows 2003/2000 platform, DTS, SSIS, SSRS, Power BI.

Confidential

Data Analyst

Responsibilities:

• Analyze and Preparedata, identify teh patterns on dataset by applying historical models.
• Collaborating with SeniorDataScientists for understanding ofdata.
• Performdatamanipulation,datapreparation, normalization, and predictive modeling.
• Improve efficiency and accuracy by evaluating model in R.
• Present teh existing model to stockholders, give insights for model by using different visualization methods in Power BI.
• Used R and Python for programming for improvement of model.
• Upgrade teh entire models for improvement of teh product.
• PerformedDatacleaning process applied Backward - Forward filling methods on dataset for handling missing values.
• Under supervision of Sr.DataScientistperformedDataTransformation method for Rescaling and Normalizing Variables
• Developed a predictive model and validate Neural Network Classification model for predict teh feature label.
• Performed Boosting method on predicted model for teh improve efficiency of teh model
• Presented Dashboards to Higher Management for more Insights using Power BI.

Environment: R/R Studio, Python, SQL Enterprise Manager, Git Hub, Microsoft Power BI, outlook.