Actively use built-in data analytics models on CDP, including customer segmentation, churn prediction, propensity modelling and next-best activity; continuous update and adjustment of model parameters and performance tracking of the models on CDP
Build advanced predictive analytics models offline by using programming languages such as R, Python, SQL and implement those models to CDP to generate them; score the model in other tools and integrate the results to CDP if the model is too complex to be generated on CDP; leveraging infrastructure including Cloud computing solutions and relational database environments
Stay on top of changing conditions by retraining and testing the model regularly; enhance it whenever necessary
Monitor models’ accuracy to catch any degradation in its performance over time
Apply expertise in quantitative analysis, data mining, and the presentation of data to see beyond the numbers and understand user interactions
Actively seek and identify where data science can provide value to business areas across the BUs, provide solutions to BUs analytical problems and validate the solution performance
Generate actionable insights and predictive modeling techniques to modify consumer behavior
Active use of built-in reporting tools on CDP; day to day report generation and maintenance using BI tools and be involved in any new tool selection and deployment
Engage with stakeholders to ensure that data insights are effectively communicated through the most appropriate data visualization and navigation tools
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