Career transition from Engineer to Finance to Data Science (02:30): Phl Bangayan started as an engineer at JPL but soon realized that needed a business background so went and got an MBA. After joining in Finance at Disney, Phil got an opportunity to shift into Marketing which led to an Analytics opportunity at Universal studios. From there, Teradata opened an opportunity to get into Data Science.
Analytics courses (07:00): As Phil started making decisions based on Analytics, he went to get formally educated on Data Analytics. MOOCs are a great way to learn but only 4% of the people who start a course actually complete it. Phil’s advice is that what works for him may not work for others regarding courses. In general, University offered courses are a better bet.
Move into Data Science (14:00): With an Analytics background, Phil now felt confident to move into Data Science. That is when Teradata opportunity opened up.
If no one understands a model, no good (18:00): It doesn’t matter how sophisticated your model is if no one understands it. And one of my pet peeves is when you do about 99% of the work, and then the work goes to waste because someone doesn’t agree with you, or because you forget to do that final part, which a lot of time is communicating it. And so I don’t believe that every data scientist has to be the best communicator, but I do believe that there has to be someone on the team who can take some analytic analysis and, and distill it to a point where an executive, like a CMO or CFO or president can, who doesn’t understand it can say, okay, what does this mean for me?
Data science needs complementary skill sets (21:00): In Data Science teams, define who you are, and don’t go off and compete against people who are more qualified than you are. So, for example, when I was going from marketing to data science, I knew that I did not want to compete against the 28 year old with a PhD in statistics. That person can write better code than me and can do better proofs than me and not a good, not a good competition. However, I bring a lot of experiences that someone who’s in that situation might not.
Last mile problem – Closing the deal (23:00): So if someone brings it 90% of the way through, I can help with that 10% and close the deal, or I can go ahead and make that work actionable. So that’s, so it doesn’t go to waste and, and no one wants to see their hard work go to waste. In fact, is one of my leadership pillars. I always tell my team that everyone plays a part it’s very easy when you’re the data scientist, to be able to say, well, hold it, I’ve got the hardest here because no one can do what I do.
Resources mentioned in this episode:
Podcast website: https://datatransformerspodcast.com
Episode Title : Data Science teams need people with multiple skill sets
Episode Summary: Data science career path doesn’t have to be purely technical. A data science team needs multiple skill sets. In this episode, Phil Bangayan, Principal Data Scientist at Teradata, talks about his career path from an electrical engineering background to MBA to Finance to Marketing and Data science. Phil talks about the need for the data science team to be able to communicate the outcomes of models in an understandable manner with CXOs. Phil also talks about the need for data science teams to have people with multiple skill sets.
Data Transformers Podcast
Join Peggy and Ramesh as they explore the exciting world of Data Management, Data Analytics, Data Governance, Data Privacy, Data Security, Artificial Intelligence, Cloud Computing, Internet Of Things.
