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Einstein Analytics & Machine Learning Model

  1. Led the application team in the implementation of Data Science, Machine Learning Model using Sales cloud computing for Generation of Einstein Analytics Predictions and Insights

  2. Leveraged the Tableau CRM Plus capabilities in building the Recipes,Story,Generation of Insights and Predictions and successful deployment techniques.

  3. Collaboration with Technical & Business Architecture teams, Environment and Release Team for sprint planning and User Story Creation

  4. Deployment of Classification Analytical Model in Production and Maintenance through Automatic Refresh and Anchored the Build of Lightning Flows to update Scores and other results in Custom object

ML Classification & Numerical Prediction Python Model

  1. Demonstrated ability in Data Modeling and Guided the Data Engineering team in Data Extraction process- Reviewed and resolved SQL stored procedures issues.

  2. Collaborated with cross-functional teams to define project requirements and translate business problems into data science solutions

  3. Conducted exploratory data analysis and applied statistical techniques to identify patterns and trends in large datasets

  4. Removed Data Anomalies and applied suitable Machine Learning and feature selection techniques for the model.

  5. Presented findings and recommendations to stakeholders through PowerBi-Visualization widgets along with predictions for unknown data

Other Projects

1

Salesforce Data Modeling and Dashboard

  • Recommendation and Decision Making Tool for Deal Pricing Team

  • Data Modeling and Data Preparation

  • Estimating Pricing Ranges across the segments

  • Historical Pricing Insights through Salesforce Dashboard Capacity

2

PowerBI Dashboard

  • Optimization engine for devising strategy to revise the targets

  • Insights on Historical Deal Targets and Recommendations for Revision of Targets

  • Comparison of Actual vs Target Metrics across various segmentations

3

Data Analytics and Data Governance

  • Generation of Risk Weighted Average Parameters and Basel III Implementation 

  • Guidance on Data Modeling Requirements  to the data ingestion, acquisition and engineering teams 

  • Data descriptive statistics for credit risk portfolios, Trend Charts and Visualizations

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