Zach has been working as a data scientist at an industry leading data consulting firm for the past 2 years. He works in fraud analytics space where he and his team has saved hundreds of millions of dollars of federal dollars using sophisticated data science techniques.
When I met him, I was really impressed with his ability to speak “real world” data science and later I found out that he has a professional background in teaching complex topics like physics and calculus, which is what makes him such a good communicator in this field.
I sat down with him on a sunny Saturday afternoon to discuss one of the most exciting projects he has worked on in his data science career.
Here’s a quick recap of what we discussed:
- Can we predict what pitch is going to be thrown next in major league baseball? Implications for Hitters (batters) equipped with this data is $10M to $15M per season.
- A wave of In-game analytics about to hit the sports industry. This in-game analytics may eat ‘Moneyball’ style static analytics for breakfast
- Are better pitchers tough to predict? Or are they just as easy to predict as others?
- What’s the correlation between a pitcher’s ERA and his predictability? ERA is a baseball metric – earned runs average – its used to gauge how well a pitcher is doing in a season.
- Is it better to be 90% accurate 30% of the times or 30% accurate 90% of the times?
- What has Ashton Kutcher to do with Data Science and Social Good?
- How to cultivate the presence of mind when communicating about data?