Responsibilities:• Working closely with marketing team to deliver actionable insights from huge volume of data, 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.• Consumer segmentation and characterization to predict behavior.• Developing, prototype and test predictive algorithms• Used different statistical models like regression and classification models to create contact scoring models.• Interpreting data analyze results using statistical techniques and provide ongoing reports.• Acquiring, cleaning and structuring data from multiple sources and maintain databases/data systems.• Identifying, analyzing, and interpreting trends or patterns in complex data • Outlier detection using high-dimensional historical data.• Characterizing false positives and false negatives to improve model for predicting customer churn rate.• Developing and implementing data collection systems and other strategies that optimize statistical efficiency and data quality.Projects Summary EMOB sales prediction – Developed predictive model for Early month on books customers (EMOB) Which predicts propensity to buy for next 6 months. This model was implemented for various portfolios resulted $10M incremental sales. Attrition Analysis – Developed an algorithm which identifies silent attritors. This strategy was implemented for various portfolios where customer retention was challenge. These Retention campaigns resulted in $4M additional incremental sales and 3% incremental Actives• Environment: Python- Pandas, Numpy, Scikit-Learn, TensorFlow - ANN, SciPy,Seaborn, Matplotlib, SQL

Responsibilities:• Participated in Status meetings with clients, conducting reviews of deliverable's and defect tracking.• Designed data model from scratch to assist team in data collection to be used for Tableau visuals.• Generated Tableau Dashboard with quick/context/global filters, parameters and calculated fields on Tableau (8.x) reports.• Used Joins, correlated and non-correlated sub-queries for complex business queries involving multiple tables from different databases.• Developed data dictionary for project coordination; created SQL create, alter, and insert scripts.• Acquiring access to data to support data analysis work.• Performed data analysis and profiling of source data to better understand source systems• Created E/R Diagrams, Data Flow Diagrams, grouped and created tables, validated data, identified lookup tables.• Redefined attributes and relationships in reverse engineered model and cleansed unwanted tables and columns as part of Source Data Analysis responsibilities.• Performed legacy application data cleansing, data anomaly resolution and developed cleansing rule sets for ongoing cleansing and data synchronization.• Performing Data Profiling, Data Cleansing, Data Transformation, Analysis and Reporting.• To implement machine learning techniques such as Linear Regression, Logistic Regression, Decision Tress, Naïve Bayes,.• Ensemble models in SAS and Python.• Evaluate and validate models with test data and chose best option.• Extensive use of Python Machine Learning libraries NumPy, Pandas,keras, Pytorch,Scikit-Learn, Matplotlib, Seaborn, Stats models, PySpark, Deeplearning4j etc.

Responsibilities:• Supported sales forecasting & planning team by improving time series & principal component analysis.• Utilized machine learning techniques for predictions & forecasting based on the Sales training data.• Executed overall data aggregation/alignment & process improvement reporting within the sales dept• Managed Data quality & integrity using skills in Data Warehousing, Databases & ETL.• Monitored and maintained elevated levels of data analytic quality, accuracy, and process consistency.• Assisted sales management in data modeling.• 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: R, QlikView, Python, Machine Learning, SQL.