Data-Guided Discovery

A generalist combining intuitive creativity with over 10 years of applied data science experience and a passion for generating value and knowledge with technology. I've lead expert teams creating state of the art solutions for clients across multiple industries. 

Projects combined multiple data science subspecialties - natural language processing to drive computational knowledge, dynamic network graphs applied to economics and financial markets, Markov processes for disease progression, automated statistical analysis of clinical trial pharmacology data, machine learning methods to assess credit risk and fraud, remote computational inference for disaster remediation, etc. For the past 5 years I've been constructing causal multi-model simulations of sport events to identify betting value.

At their core machine learning (ML) and AI are nothing more than collections of algorithms - many of which are open source and available to anyone. There is no magic algorithm that we can throw at data to extract value. Unfortunately, the data will not wrangle itself. 

Every day the buzz behind ML and AI increases, yet most people using these terms have no working knowledge of what they mean. Without a considered approach and intelligent design these techniques simply cannot deliver useful results.  

Applied data science requires more than knowledge of the latest ML trends or advanced statistics. To truly drive positive change requires the ability to dive deep into a problem space and understand the challenges, collaborate with key knowledge holders to operationalize their talent, and  intelligently deciding on the right data science technology for the job.