For many real-world commercial problems, the best approach is not machine learning or statistics; it's Mathematical Optimization. In today's episode, hear all about optimization from the guru Jerome Yurchisin.

Jerry's an extraordinarily clear communicator of complex topics and a world-leading expert on real-world applications of mathematical optimization. He:

• Works as a Data Science Strategist at Gurobi Optimization, a leading decision-intelligence company that provides mathematical optimization solutions to the likes of Uber, Air France and the NFL (indeed, a wild 8 out of 10 Fortune 10 companies use Gurobi!)

• Previously spent eight years as a mathematical consultant where he paired mathematical optimization with machine learning, statistics and simulation to inform decision-making.

• He was also previously an instructor at the University of North Carolina at Chapel Hill, where he obtained his Master’s in Operations Research and Statistics.

• He holds an additional Master’s in Applied Math from Ohio University.

Today’s episode may appeal most to hands-on practitioners like data scientists and ML engineers, but it does also have tons of content that will be of interest to anyone who’d like to leverage data to make better commercial decisions or optimize commercial processes.

In this episode, Jerry details:

• What mathematical optimization is.

• The kinds of real-world problems where mathematical optimization is a far better approach than a machine learning or statistics approach.

• The history of mathematical optimization including why it wasn’t popular until recently.

• The cutting-edge hardware and software innovations in mathematical optimization today.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.