Maybe you’ve heard this advice before, but she explained that, especially in a data science context, continually asking “why” can be truly strategic: “Because, a lot of times, [with] a legacy process or a model, if someone can’t explain it to you, that means that there are probably issues that you can find in it that you can fix.” As a chronic question-asker, I fully endorse this advice. (View Highlight)
You can learn a lot about the potential opportunities in your domain by asking how things work and why they’re done the way they are, but you won’t get there by pretending you know everything already. Yep, you’re going to have to be a little vulnerable. (View Highlight)
Approaching problems from the mindset of “I don’t think I know anything, tell me all the stuff, how do I find out all the information” can be so much more helpful (and freeing?) than the self-induced pressure of thinking, “I should know how to do this.” Releasing the pressure to be perfect while also getting ahead and making yourself stand out? Win-win. You can listen to Jamie’s take here. (View Highlight)
New highlights added November 4, 2024 at 11:02 AM
He said it’s easy to get pulled in a lot of different directions as a founder, and that’s something many people can relate to, I think, even if they’ve never founded a company. I certainly can. He explained that it’s beneficial to figure out the difference between what you truly like to do and what people are putting upon you or what you feel like you “should” do. I feel this on a deep level. It took me years to realize that I needed to go against the grain of everyone else’s expectations if I was going to be happy. (View Highlight)
In her Hangout episode, Emily Riederer, Senior Manager of Analytics & Data Science at Capital One, was asked how to think about transitioning into data science roles with the knowledge that many data practitioners aren’t getting to spend much time, if any, building fancy machine learning models. (View Highlight)
Her (stellar) response was to keep in mind that the parts of data science that get the most hype and the most focus in schools may not be the most satisfying parts. She said, “It’s also really good to recognize, you can learn a ton in pretty much any role.” (View Highlight)
She went on to say that “in school, we spend most of our time learning hyperparameter tuning,” but “that often isn’t the hard part or, sometimes, even the most interesting part.” She recommends “being open with that kind of growth mindset of, whatever job you end up in, there’s going to be a ton to learn, and a ton of really interesting work to be done.” (View Highlight)
“I think being comfortable with the word no is really important,” says Ben Arancibia, Director of Data Science at GSK. “Ruthlessly prioritizing your time” will prevent being overloaded and help ensure that you’re focusing on things you’re passionate about, but it might just have an added benefit you wouldn’t expect, Ben explains. (View Highlight)
“If you think about your [personal] brand management, it’s sometimes nice to be wanted. So, saying ‘no’ sometimes is good for that brand management.” Confidently and honestly saying no to things that aren’t the right fit for you might be daunting at first, but it’s worth it. (View Highlight)
Ben also recommends being radically transparent. In radical transparency, he says there’s “no such thing as holding cards close to your chest. You basically say, here’s what I’ve got. What do you have?” He says the open and honest conversations you’ll have will lead to greater vulnerability, “which is how you make connections with people and are really able to be empathetic with people and trying to understand their problems.” If you know anything about me, you know I wholeheartedly agree with him on this. You can listen to Ben’s advice for yourself here. (View Highlight)
