Value Driven Data Science: Boost your impact. Earn what you’re worth. Rewrite your career algorithm.

[00:00:00] Dr Genevieve Hayes: Hello and welcome to Your Value Boost from Value Driven Data Science, the podcast that helps data scientists transform their technical expertise into tangible business value, career autonomy, and financial reward. I'm Dr. Genevieve Hayes, and I'm here again with Dr. Sayli Javadekar, a data scientist and economist who has recently transitioned from academia to industry to turbocharge your data science career in less time than it takes to run a simple query.
[00:00:34] In today's episode, we'll explore the powerful transferable skills that academic trained data scientists bring to industry, and how any data scientist can develop these same strengths to boost their impact. Welcome back, Sayli.
[00:00:50] Dr Sayli Javadekar: Good to see you again.
[00:00:51] Dr Genevieve Hayes: in our last episode, Sayli you and I discussed the struggles of transitioning from academia to industry as a data scientist. But here's the thing, while that transition can be brutal. Academics don't show up in industry empty handed.
[00:01:07] So today I wanna flip the script and focus on what academic data scientists bring to the table and what non-academic data scientists can learn from them. Sayli, now that you've been in industry for a while, what skills from your previous life as an academic have you found to be most valuable in your day-to-day work?
[00:01:30] Dr Sayli Javadekar: There have been a couple of things that I think that academia really teaches us and sort of skills that are transferable to the industry. The number one skill, is to communicate technical stuff to technical people. The second thing, I think very important thing is how to structure. An ambiguous problem because in research we do begin with ambiguity. We come up with our own research question. We come up with our own hypothesis. We collect our own evidence data. We prove or disprove the hypothesis, and then we communicate to different stakeholders.
[00:02:05] It could be our colleagues, it could be students, it could be the funders. In industry. Sometimes it can lack a lot of structure and to know where to begin with it can be a bit of a struggle and that is something that Academy data scientists are very good at
[00:02:22] and that is definitely a value addition. Thirdly, I think is documentation. We are very good at documentation. That is something I realized a skill that academia really teaches us because we do document our results in a paper or for conferences or for other sort of non-academic communication.
[00:02:43] Could be a press release, a blog, or whatever. That is something that. At times in industry, or at least the limited experience I have had, I feel that some folks lack and that's something that as academics, we are very good at documenting all of this in a very structured way. And lastly, I think a skill that in the industry very underrated, is teaching. So teaching is a skill that we develop a lot in, academia and as you progress in your career it is a skill that will be very helpful to train new colleagues to talk to junior colleagues, to talk to folks who are just joining the industry.
[00:03:21] I think that is a skill also as being a mentee now as you realize how important mentorship as a skill is. And therefore I really value my time as. A teacher in academia. And that is a skill that I developed over the years.
[00:03:38] Dr Genevieve Hayes: The biggest one for me is that whole experimental mindset.
[00:03:42] Dr Sayli Javadekar: Yes.
[00:03:43] Dr Genevieve Hayes: To this day, whenever I'm faced with a problem, the first thing I do is come up with a series of research questions and then I try and answer those. And that's just, standard practice. If you're an academic, but you don't see anyone doing that who doesn't have that academic background.
[00:04:00] Dr Sayli Javadekar: Yes, exactly. And to structure it in an organized way. I mean, non-academics of course do it, but I think it's not in a organized way. It doesn't follow a scientific pattern to reach to the conclusion. And the other thing I think academics really have is a lot of resilience.
[00:04:17] We've seen a lot of failure in our, of time we've spent in academia. Rejection is pretty commonplace for us. It is okay for us to fail because we know how to pick ourselves up and, move on to sort of keep going even when things get tough. So that's, another. Very important skill that academia really bestows on us.
[00:04:37] Dr Genevieve Hayes: One of the things that you said you struggled with in our previous episode was the pace of industry. And, I was talking about the challenges I found initially with juggling multiple projects, but one of the things that's the flip of that is because.
[00:04:54] As an academic, you know, doing your PhD, you're doing the one project for three or four years, it teaches you to persist with massive projects and there probably aren't that many people in industry who have dedicated their life to just one project for three or four years often without seeing any results.
[00:05:17] Dr Sayli Javadekar: Yeah, exactly. Yeah. That's definitely a character building thing that academia does for us.
[00:05:24] Dr Genevieve Hayes: yes. Let's call it character building. Yeah. So now obviously the easiest way to get all these skills is to go and spend three or four years of your life doing a PhD, but I'm guessing, a lot of our listeners. Don't want to do that or might not have the opportunity to do that. Do you think that these skills are something that someone who hasn't been in academia or hasn't done a PhD could potentially develop at least to a certain extent in their own careers?
[00:06:00] Dr Sayli Javadekar: Yeah, definitely. I think spending a decent amount of time in a particular field. You eventually, invariably end up developing some of those skills. People in the industry also do face failures and they do overcome those failures and move on in life. They do juggle with ambiguous problems.
[00:06:22] Perhaps developing an experimental mindset could be something very different or something that only the academics have. And perhaps communicating with academics would help them develop that sort of mindset. Working along with other academics will help them develop that sort of mindset.
[00:06:41] Yeah, I think they can overall develop these skills.
[00:06:45] Dr Genevieve Hayes: In our last episode, we were talking. About the importance of ex academic data scientists, finding business mentors to help them with their transition. If you are not from an academic background, it seems like the logical solution is to find a academic data scientist and ask them for mentorship.
[00:07:06] It could become a two-way conversation.
[00:07:09] Dr Sayli Javadekar: Yeah, that sounds like a perfect. Solution because there's so much that each of them can learn from each other. And , it's a win-win situation for either parties.
[00:07:19] Dr Genevieve Hayes: Is your team at work a combination of, well, yourself, who's an academic and people who are lifelong industry data scientists.
[00:07:27] Dr Sayli Javadekar: Yes. So we are. A combination of me with an academic data scientist as well as we have people who have only been in the industry. And at this point I feel I'm learning so much from them. Because I'm also relatively new and I'm trying to find my place in the industry. But I do feel that hopefully at some point they will tell me that they've learned something from me as well.
[00:07:51] Dr Genevieve Hayes: I was surprised by that when, I left one of my jobs and one of the people who I had managed in that job actually made a comment like that, and he said, he'd learned these things that I taught him, that came from my academic background and it's like, wow, that's really cool.
[00:08:08] That was useful. Yeah. So yeah, mentorship's the way to go in both directions.
[00:08:14] Dr Sayli Javadekar: Exactly. I think that's how any business works, right? You need people, business ultimately is people. So, working together and collaborating together, you create a great product.
[00:08:25] Dr Genevieve Hayes: So I think the conclusion of our two episodes is if you're an academic, reach out to non-academic data scientists and learn from them. And if you're a non-academic data scientist, reach out to academic data scientists and learn from them.
[00:08:40] Dr Sayli Javadekar: Exactly. Yeah.
[00:08:42] Dr Genevieve Hayes: And that's a wrap for today's value boost. But if you want more insights from Sayli, you are in luck.
[00:08:48] We've got a longer episode where we discuss some of the key challenges data scientists face when transitioning from academia to working in industry and practical strategies to help data scientists develop the business acumen needed to succeed. And it's packed with no nonsense advice for turning your data skills into serious clout, cash, and career freedom.
[00:09:13] You can find it now wherever you found this episode. Or it's your favorite podcast platform. Thanks for joining me again, Sayli
[00:09:21] Dr Sayli Javadekar: Thank you very much for having me.
[00:09:23] Dr Genevieve Hayes: and for those in the audience, thanks for listening. I'm Dr. Genevieve Hayes, and this has been Value-Driven Data Science.