Country: United States of America
Position: Data Science Engineering Architect
As Data Science Engineering Architect at DataRobot, Mark designs and builds key components of automated machine learning infrastructure. He contributes both by leading large cross-functional project teams and tackling challenging data science problems. Before working at DataRobot and data science he was a physicist where he did data analysis and detector work for the Olympus experiment at MIT and DESY.
Across a large number of diverse industries, modelers and developers are being asked to generate accurate forecasts for complex and dynamic systems. Traditional ways of attacking these problems are not able to deliver results but recent developments across the whole modeling life-cycle open opportunities for solutions that can. In this talk, I will cover challenges of time-series problems, traditional approaches to attack these problems, why those approaches fail and how we can use machine learning to build systems that can succeed.