Predictive modeling:

Develop predictive models using a variety of statistical and machine learning techniques to help forecast future trends and patterns, identify key drivers of business outcomes, and support data-driven decision-making.

Artificial Intelligence:

Use of conversational AI (LLMs) to get insights from corporate data and process automation.

Model evaluation and selection:

Use rigorous testing and validation methods to assess the effectiveness and accuracy of predictive models and identify the most appropriate model(s) to use for a given business problem or use case.

Deployment and ongoing monitoring:

Integrate predictive models into existing business processes and systems, and continuously monitor their performance and accuracy over time, making updates and adjustments as needed to ensure continued value and impact for the organization.