SUMMARY

• Savior of data with 6 + years of experience in data related field encompassing a wide range of skill set.
• Over 3 + years of experience with Machine Learning using Python. I leveraged packages like Sklearn, Tensor flow, Pandas in python to understanddataand developing applications
• Worked on Amazon Web Services cloud computing environment.
• Experienced in SQL programming and creation of relational database models.Strong with ETL,Datawarehousing,DataStore concepts
• I employed Spotfire, Tableau to create dashboards and visualizations.
• Strong experience working with SQL Server 2008, Studio R and python.
• Worked on Statistical models to create new theories and products.
• Experience working with statistical and regression analysis, multi - objective optimization.
• Designed and implemented supervised algorithms like logistics Regression, Decision trees, SVM’s, polynomial regression and unsupervised machine learning algorithms like clustering. k-means. mixture models. hierarchical clustering, anomaly detection.
• Worked with clients to identify analytical needs and documented them for further use. Identified problems and provided solutions to business problems usingdataprocessing,data visualization and graphicaldataanalysis.
• Developed predictive models using Decision Tree, Random Forest and Naïve Bayes.
• Solid knowledge of mathematics and experience in applying it to technical and research fields. Identifying areas where optimization can be efficient.
• Developed predictive models using Python to predict customers churn and classification of customers.
• Query optimization, execution plan and Performance tuning of queries for better performance in SQL.
• Worked on Shiny and R application showcasing machine learning for improving the forecast of business.
• Worked on Designing and configuration of the database and back end applications and programs.

TECHNICAL SKILLS

Programming skills: Python (SciPy, NumPy, Pandas, Scikit-learn, TensorFlow, Flask, Matplotlib, Bokeh, Jupyter notebook), C, R, Shell Scripting, SQL

Machine Learning: Supervised learning, Unsupervised learning, Reinforcement learningNeural networks and Deep learning.

Math and Statistics: Inferential Statistics, Differential Equations, Intermediate Probability, Stochastic Calculus

Tools: and Technologies: MATLAB, Minitab, Microsoft Office Suite, Eclipse, R studio, Tableau, Spotfire, Hadoop, HDFS, Apache Spark and Hive, Scoop, ETL tools, QlikView

Data Bases: SQL Server, Oracle, MySQL, NoSQL

PROFESSIONAL EXPERIENCE

Confidential, Dallas TX

Data Scientist

Responsibilities:

• Working closely with marketing team to deliver actionable insights from huge volume ofdata, coming from different marketing campaigns and customer interaction matrices such as web portal usage, email campaign responses, public site interaction, and other customer specific parameters.
• Characterizing false positives and false negatives to improve a model for predicting customer churn rate.
• Consumer segmentation and characterization to predict behavior. Analyzing promoters and detractors (defined using Net Promoter Score).
• Outlier detection using high-dimensional historicaldata.Acquiring, cleaning and structuringdatafrom multiple sources and maintain databases/data systems.Identifying, analyzing, and interpreting trends or patterns in complexdatasets.
• Developing, prototype and test predictive algorithms.Filtering and "cleaning"dataand review computer reports, printouts, and performance indicators to locate and correct code problems.
• Developing and implementingdatacollection systems and other strategies that optimize statistical efficiency anddataquality.
• Used different statistical models like regression and classification models to create contact scoring models. Also used clustering to the customerdataprofiles to do customer segmentation and analysis.
• Interpretingdata, analyze results using statistical techniques and provide ongoing reports.
• Building a recommender system based on client’s past renewal history to upsell and cross-sell them other related products or services.Created a recommendations engine that finds related customers, products.

Confidential - Pittsburg |Kansas

Data Science

Responsibilities:

• Supported sales forecasting & planning team by improving time series & principal component analysis.
• Utilized machine learning techniques for predictions & forecasting based on the Sales trainingdata.
• Executed overalldata aggregation/alignment & process improvement reporting within the sales dept.
• Managed Data quality & integrity using skills inDataWarehousing, Databases & ETL.
• Monitored and maintained elevated levels of data analytic quality, accuracy, and process consistency.
• Assisted sales management indatamodeling.
• Ensured on-time execution and implementation of sales planning analysis and reporting objectives.
• Worked with sales management team to refine predictive methods & sales planning analytical process.
• Executed and monitored the accuracy and efficiency for sales forecasts & reporting.
• Prepared Dashboards using calculations, parameters in QlikView.
• Supported consistent implementation of company reporting and sales process initiatives.
• Used Python to identify customer classification, tree map, and regression models.
• Performed forecasting and time series analysis of customer likes and dislikes.

Environment: ETL, QlikView, Python, Machine Learning, SQL

Confidential

Data Analyst / Data Engineer

Responsibilities:

• Characterizing false positives and false negatives to improve a model for predicting overpaid claims, Consumer segmentation and characterization to predict behavior.
• Outlier detection using high-dimensional historicaldata.
• Analyzing promoters and detractors (defined using Net Promoter Score).
• Installed and configured Hadoop MapReduce, HDFS, Developed multiple MapReduce jobs in Python fordatacleaning and preprocessing.
• Supported Map Reduce Programs those are running on the cluster. Involved in loadingdatafrom UNIX file system to HDFS.
• Experienced in managing and reviewing Hadoop log files.
• Involved in writing Hive queries to load and processdatain Hadoop File System.
• Exporteddatafrom Impala to Tableau reporting tool, created dashboards on live connection.
• Gained very good business knowledge on health insurance, claim processing, fraud suspect identification, appeals process etc.
• Maintained integrity of the database by implementing different validation techniques to thedata uploading procedures.
• Defended conjunction between ERP system and database using SQL reporting services.
• Developed dashboards in Excel with SQL connections to ensure smooth procedural flow and analysis.
• Identified and proposed process enhancements.
• Quantified multiple stocking proposals and contracts with customers which increased business by approximately 20% per customer.
• Developed ad-hoc reports upon requests using MS Access and MS Excel.

Environment: Hadoop, MapReduce, Hive, Impala, Python (pandas, NumPy, scikit-learn), SQL Server, Excel, Tableau

Confidential 

Data Analyst

Responsibilities:

• Identifying what factors could influence the overall satisfaction of consumers. Range (1-5).
• Considered the SMG (Service Management Group) database survey results in analyzing the impact on overall customer satisfaction.
• Analyzed business process workflows and assisted in the development of ETL procedures for mappingdatafrom source to target systems
• We used Ordinal logistic regression methodology in explaining the importance of features.
• The analysis involved predicting the overall satisfaction - ordinal rating, by analyzing the impact of each independent factors in explaining the output.
• Packages used: MASS package, plot function for Ordinal logistics regression model.

Environment: R Studio, SQL Server, Dplyr, Tidyr, ggplot2, Tableau, MASS package - plot function.

Confidential

Responsibilities:

• Responsible for Data collection and data preparation and normalizing thedata.
• Used SQL, ETL tool, R Studio, and Python fordatapreparation.
• Supported data consultants in the data modeling phase.
• Used Constraint optimization algorithms (USED EXCEL) to optimize the marketing budget.
• Used Time Series Models - decomposition of time series, trend, and seasonality detection, forecasting and exponential smoothing in predicting the market share and brand share to allocate the Marketing budget

Environment: R, Python (pandas, NumPy, scikit-learn), SQL Server, Excel, ETL.