1. Analyzing and exploring data to extract meaningful insights.
2. Developing predictive models using supervised learning techniques, such as classification, regression, and forecasting. For example, predicting client intent by analyzing email content.
3. Building models to identify underlying patterns through unsupervised learning techniques, including clustering and dimensionality reduction; for instance, identifying client peer groups.
4. Maintaining a critical perspective and demonstrating the ability to challenge existing methodologies.
5. Assessing the needs of internal stakeholders to address operational and strategic requirements in complex data analysis projects.
6. Addressing business challenges by analyzing both internal and external data sources.
7. Establishing and refining data analysis models to ensure optimal performance.
8. Demonstrating a commitment to staying informed on developments.
9. Other ad-hoc supports request by manager.
