My primary interests are in business intelligence, data engineering, statistical modeling, and causal inference. I have learned from experience that a data and analytics team requires collaboration across different concentrations: a data scientist can only build a successful machine learning model if the data used to train the model is accurate. The model only works if its purpose is clear and it solves a real business problem. And that model will only be trusted if it is presented in an easy to understand way.

I am fortunate to have a background in roles that sit at the intersection of these disciplines, both at startups companies and large financial institutions. It’s exciting to be so heavily involved in the strategic planning and growth of a business, and I have found these topics I mentioned to be top of mind for business leaders. These are ever-changing, innovative skills that allow for lifelong learning, perfect for teaching to the next wave of professionals coming through our applied analytics program.