Data Scientist

• Responsible for designing, building and supporting data models by using real-time and past analysis to support security, business, technical planning, and decision making
• Organizing power-bi dashboard with the real-time data and the report gives the list of non-valuable signatures
• Developed pipeline to understand the relation between ancestor and descended
• Wrote python scripts to make a pragmatic pipeline to recognize and visualize the data
• Created pipeline with various analysis metrics using data that returns metrics values on how significant the feed is to purchase.
• Developed various visualizations in python and developed python scripts to export all metrics analyzing visuals in PDF format.

• Developed a machine learning model for private extraction fields
• Outlined metrics for training and prediction
• Performed optical character recognition
• Examined the business data and conducted various statistical tests
• Developed the different regression models after finding the test
• Recognized various projection types and integrate model
• Responsible for automating the regression process where audit team can provide report in the tool and determined coefficients and the significance levels of the data
• Migrated C++ code to python by using statistical methods
• Arranged python code in Microsoft Azure
• Created functions in Microsoft Azure and responsible for testing in advanced rest client

• Prepared forecast in stationary models using exponential smoothing
• With the help of smoothing technique removing the random variation (noise) from historical demand in demand forecasting to get a more realistic idea about what is “really going on”
• Identified command patterns such as primarily trend, seasonality and demand levels that can be used to estimate future demand from the products received
• Executed quantitative analysis of product sales trends to suggest pricing decision
• Examined models by over fitting and residuals errors as a diagnostic process
• Analyzed and forecasted time series data by using ARIMA model
• Using over fitting and residual errors to diagnose a fit ARIMA model
• Corrected regression model to time series data, to find autocorrelation in the residuals
• identify with the seasonal changes or fluctuations in demand that affects business
Completed predictions for new product demand

• Coordinated in developing predictive models and strategies for valuable fraud detection for credit and customer banking activities using K-means clustering
• Supported peer group for text mining on customer review/comment data, by using topic modeling and sentimental classification, using deep learning algorithms
• Developed large scale customer data and analytics using advanced machine learning and statistical models relying for issuing loan using random forest using R
• Executed K-means clustering in order to understand customer backgrounds and segment the customer based on the customer transaction behavior information for customized product offering, customized and priority service, to improve existing profitable relationships and to avoid customer churn etc. using R
• Worked on interactive dashboards for creating story and presenting to business using Tableau
• Operated analytical applications like SAS to determine trends and relationships between different pieces of data
• Conducted dry analysis into an presented story that influences the direction of the business and communicating with diverse teams to take a project from start to end
• Conducted cost and benefits analysis on new ideas
• Scrutinized and tracked customer behavior to identify trends and unmet needs
• Proven leadership protagonist for specially assigned projects, managing and completed tasks within deadlines
• Actively involves in collaborating and communicating with team members to complete research and information effectively.

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