Richad Nieves-Becker is a self-taught data scientist with an eclectic background. He currently leads the data science function at Revantage, a real estate shared service organization in the Blackstone family. He got a BA in Neuroscience and Anthropology. And was on the PhD path until he realized it was not for him. He pivoted and earned a Masters in Commerce from the University of Virginia. He started at CoreLogic focusing on text mining, then moved to Greystone leading all things data in an innovation lab. He credits his career progress to focusing on impact and deeply understanding the business case.
In this episode, we cover a range of topics including:
- His entry into data science
- Academic vs business work
- How he cold emailed his way to getting job interviews
- Why data scientists need sales skills
- How data scientists should think about building a portfolio
- Defining tractable problems in machine learning and data science
- Moving from mathematics to data science
- Framework for creating educational content
- Creating a course for data scientists
- How he interviews people
- The report he's writing for new data science leaders
- Building good culture by aligning an individual's desires to the company's goals
- The advantage of modular products
- Rise of MLOps and data versioning
- Monetization of models
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By Prateek Joshi
4.9
88 ratings
Richad Nieves-Becker is a self-taught data scientist with an eclectic background. He currently leads the data science function at Revantage, a real estate shared service organization in the Blackstone family. He got a BA in Neuroscience and Anthropology. And was on the PhD path until he realized it was not for him. He pivoted and earned a Masters in Commerce from the University of Virginia. He started at CoreLogic focusing on text mining, then moved to Greystone leading all things data in an innovation lab. He credits his career progress to focusing on impact and deeply understanding the business case.
In this episode, we cover a range of topics including:
- His entry into data science
- Academic vs business work
- How he cold emailed his way to getting job interviews
- Why data scientists need sales skills
- How data scientists should think about building a portfolio
- Defining tractable problems in machine learning and data science
- Moving from mathematics to data science
- Framework for creating educational content
- Creating a course for data scientists
- How he interviews people
- The report he's writing for new data science leaders
- Building good culture by aligning an individual's desires to the company's goals
- The advantage of modular products
- Rise of MLOps and data versioning
- Monetization of models
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