"Modeling Geopolitical Tail Risks" and "Building Strong Data Science Teams"

Data Science DC is bringing you two talks this September.

From Dr. Iris Malone, Director of AI and Data Science for GeoQuant: “Modeling Geopolitical Tail Risks”

From Brian Sacash, Director of Data Science at Redhorse: “Building Strong Data Science Teams”

Descriptions

Modeling Geopolitical Tail Risks

This presentation will provide a project overview of modeling geopolitical tail risks. Despite growing attention around rare, but highly consequential, geopolitical events like the prospect of a China-Taiwan war, escalation in the Middle East, or an end to the Russia-Ukraine war, systematic, real-time monitoring of these risks remains elusive. This presentation will review the challenges in geopolitical forecasting and some possible strategies to overcome it. It will walk through an example of how to develop a tail risk model, forecast trends, and apply Monte Carlo simulation techniques to stress-test how these forecasts might change in response to different shocks.

Building Strong Data Science Teams

Creating cohesive and effective data science teams isn't just about finding smart and capable people. Brian will discuss how to navigate team dynamics and systematically build a collaborative tech culture.

Logistics

This event will be in person.

Agenda

6:30 PM - Food and networking

7:00 PM - Talks

After the talks, some folks will likely head to Courthaus Social (or somewhere else if they don't let us in)

Speakers

Dr. Iris Malone is Director of AI and Data Science for GeoQuant, a quantitative political risk firm. They lead GeoQuant’s quantitative research and product innovation efforts, including forecasting, scenario analysis, and cross-asset modeling. They have a Ph.D. from Stanford University and joint Bachelor degrees from Cornell University. Prior to joining GeoQuant, Dr. Malone was an Assistant Professor at George Washington University and Principal Investigator with a DHS Center of Excellence.

Brian Sacash is the Director of Data Science at Redhorse, where he leads data scientists and engineers in creating innovative solutions. Throughout his career, he has applied natural language processing, machine learning, big data, and statistical methods to address complex problems and uncover insights for federal clients. Brian holds a Master of Science in Quantitative Analysis from the University of Cincinnati and a Bachelor of Science in Physics from Ohio Northern University.