Memorable Quotes from the show:
[00:12:47] "The part of math that I was interested in wasn't that crazy, crazy theoretical math. It was just like, Oh, how can we use data to drive better decisions? Like how can simple statistics and computing metrics and just keeping track of shit using numbers? How can that help build better products or build better systems? And that's what I learned in systems engineering. Combine that with some of my CS classes, which got me into a little bit more machine learning, and then it started clicking in my head of like, Oh, this data thing is really cool."
Hightlights of the show:
[00:00:40] Guest Introduction
[00:03:26] Talk to us a little bit about where you grew up and what it was like there.
[00:07:57] What is it about us (of Indian heritage) and software and data science?
[00:09:11] Was there something you were always good at? Did you think you were ever going to be an author?
[00:11:03] Was data science something that you were exposed to when you're young?
[00:13:57] What is the business side of data? Please paint that picture for us.
[00:19:22] Is it better to have blank space on a resume than neutral information?
[00:23:34] LTalk to us about what this philosophy is for projects.
[00:31:57] How do we demonstrate business value with a project, especially if we don't have on the job experience and are doing a project to demonstrate our technical ability?
[00:39:20] You talk about cold emailing in your book. Is that just when someone messages somebody highly ranked on LinkedIn and leave it at that?
[00:40:50] Let's say somebody sees this awesome job on LinkedIn and then started looking for people in that company. Should they go and message an individual contributor, data scientist and have them look at their profile or send a message to the CEO? Like who on the spectrum do they reach out to?
[00:46:03] It is noticed that a lot of people that are new to the industry are new data scientists who are all up in their head thinking oh, man, like math and everything, thinking all about algorithms and their sleep. They think that these behavioral interview questions are just fluffy bullshit. Why do you think folks have this misconception?
[00:50:10] You talk about a framework in the book at a high level. Can you share a bit of that framework for how you would answer that question (where the star format doesn't apply)?
[00:52:34] Would you rather mention your knity gritty experiences from the past in an interview or do mention a little of a role that you played in math or astrophysics. Say that you're trying to get into a machine learning engineer role, can you share your response to that question with us here?
[00:55:12] Auditing the "tell me about yourself" question.
[01:04:50] What does product sense mean? What is it? Why are people afraid of it? Why does it seem like such a difficult skill?
[01:11:35] What's the number one product sense question that you see being asked?
[01:14:36] It is it's 100 years in the future. What do you want to be remembered for?
[01:16:18] What do most people think? Within the first few seconds of meeting you for the first time.
[01:16:47] You have this awesome blog post about books that you always bring up in conversations. One of them is written by probably my absolute favorite authors and one of my favorite books. That's Antifragile by Nassim Taleb. Talk to us about the three main takeaways you've gotten from that book.
[01:21:19] What are you currently reading?
[01:24:23] First question what makes you cry?
[01:24:41] If you were a vegetable, what vegetable would you be?
[01:24:50] What have you created that you're most proud of?
[01:25:33] What's the best piece of advice you have ever received?
[01:26:54] If you lost all of your possessions but one, what would you want it to be?
[01:27:29] Do you ever sing When You're Alone?
[01:27:52] What's your favorite candy?
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