#155 Tech enthusiast to AI leader: Getting to know podcast host, Felipe Flores. Part 2
In Part 2 of getting to know Felipe Flores, Felipe answers questions on what types of environments and challenges keep him energised, where he sees the data science industry heading in the next few years, and how job seekers can de-risk decision making for their potential employers.
Felipe talks about:
Using data to improve Data Futurology
The gap between University and industry
Which projects should you show to potential employers to demonstrate good foundational knowledge
Intellectual capital vs money capital, Angel Investors and the approach to start and develop a business
Strategies and decision making techniques to adapt to change
MLOps – Felipe’s view on using Feature Stores when deploying models
Enjoy the show!
Quotes
“As a hiring manager, you are worried about taking risks of the decision you are making when you are trying to hire someone. The person trying to get the job should do everything they can to de-risk the decision for the hiring manager.”
“Do I like change? Yes. Love it. I’ve changed my mind over my approach throughout my career. When I was young, I needed a new company, a new role, I needed to be moving into change constantly so I could keep learning. Wanting to see different things which is what attracted me to consulting.”
“I’ve been told a lot of times that I’m quite “meta” in the sense that I always look for the overarching patterns. For me being able to see different industries and looking for a change, I don’t focus as much on what’s different. I focus on what’s the same and I try to find frameworks that are reusable.”
“It’s one of the things I liked about finance. Finance is an abstraction about how every company works. Every organization creates financials. Finance is this language that is across everything. And once you understand that language, you can see not only into these companies but also how the economy works as it’s the interaction of different companies that are telling you what they are doing in this language.”
“Maybe now I’m feeling better about it because I’m trying to do something with the podcast to discuss the issues that are going to help us become more mature in this space as a country and hopefully beyond that as well.”
“I do see maturity coming into Australia in the last couple of years with the rise of data scientists and the rise of ML engineers. As an industry we are feeling the pain of shortage in the engineering side of data science, that’s great! That means we are thinking about scaling and serving and automated testing and introducing engineering practices around bias. That’s the place that I feel we are in now and moving from cottage industry to having models running or laptops or computers to something that can be served at scale.”
“Now start-ups understand the problem that we are trying to solve from the customers perspective and understand how to solve it with AI. That’s a huge step in maturity that I would really like to see.”
“In terms of where we are heading in the next few years, it will be around more and more business people learning data science. More businesspeople are understanding analytics but very few understand the opportunities that ML can provide. It’s definitely an area that Data Futurology focuses on by providing that knowledge and content for non-technical business leaders.”
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