902: In Case You Missed It in June 2025

0:05Jon Krohn:This is episode number 902, our In Case You Missed It in June episode.

0:20Jon Krohn:Welcome back to the Super Data Science Podcast. I am your host, Jon Krohn. this is an In Case You Missed It episode that highlights the best parts of conversations we had on the show over the past month. In episode 897, I speak to Diane Hare, who is founder and CEO at the strategy consulting firm Bizlove. Diane's business helps companies drive sustainable digital transformation, including transformation via AI. And she's got five key tips for us to do just that. In this clip, she shares them with us. You know, we've kind of, we've framed the common problems that we have in organizations around adoption of AI or any other, you know, new technology.

1:02Jon Krohn:We've talked about, you know, you talked about particular solutions, including case studies of situations where you have made a, you've been really successful making a big impact with AI and, you know, you've provided some here and there, you know, takeaways that any listener can have, I was wondering if you have a particular set of tips, a particular list of tips that would be helpful for our listeners to be able to come away from this podcast episode and more effectively enable change in their organization, to more effectively be able to not get stuck on the what and be able to accelerate change and success in the organization.

1:48Yeah. So I have five. Perfect. First one is top-down, bottoms-up, which means if you're going to push change through your organization, you have to not only get your senior leaders aligned, you need to enable those at the front lines. And your job is often to be the bridge between the two. So I say top-down, bottoms-up from an enterprise lens. The other thing is bold claims and proof points. When you're cutting through the noise and you're a storyteller and you say big claims, you have to back them up with data and proof points because not everyone is going to come with you, believe you, understand you.

2:31So you need to make sure you're expressing yourself so all different populations can believe and understand you. um the other one is what i said you have real two you have two levers to pull when you're trying to

2:46Jon Krohn:drive change you can either we're into number two now three oh we're into number three so first one top down bottoms up yeah yeah bold claims and proof points is number two gotcha yep um the next one is you have two levers to pull when you're trying to drive change you can either inspire or incentivize. Often companies double down on incentivize and they forget they inspire. So focus on storytelling to tell the broader impact, right? To describe the ROI in a way that's inspirational. Right. So I guess like, so if you're being incentivized, there's something like, you know, there's some end of your bonus that you're chasing.

3:28Jon Krohn:And so, you know, you're, you're not necessarily like inherently motivated by some challenge or opportunity. You're being extrinsically like, yeah, incentivized to go in some direction. But if you're inspired, then, you know, you kind of naturally are like, wow, this is a huge opportunity. This is the moment of my career. And you're not even worried about some specific bonus because you know that if you succeed at this, you're going to be recognized, you know, there's going to be a big impact in the firm and it's a great opportunity for you. Yeah. The impact you have is bigger than you. That's when you are inspired, you're serving someone else.

4:12You're serving a bigger cause and it taps into the discretionary effort, the nights, the weekends, the long hours, not because you have to, but because you want to. And then the next one is number four, Or focus on the 18%. Focus on the early adopters, not the naysayers. And then the last one is, it's easy and simple, but it's, they call you a leader because you have the courage to go first. When you're in change programs, you are usually the outlier. you are the person pushing against compliancy um you know the way we've always done business you're crazy we can't change and so they call you a leader because you go first you you are going to have to put yourself into a vulnerable courageous space and just know that going into it nice uh

5:11Jon Krohn:I didn't read those down. Can you recap the five for us quickly? Sure. First one, top down, bottoms up. Yeah, yeah. Second, bold claims and proof points. Yeah, yeah. Third, two levers, inspire or incentivize. Mm-hmm. Four, focus on early adopters, your 18%. Then the fifth one is they call you a leader because you go first. So have the courage to go first. I definitely went away inspired by Diane's approach. so I guess she got me at point three. The next guest is someone who specializes in advising folks who'd like to advance their career in data or even land their first data role. In episode 893, Avery Smith explains what kind of projects should go into a portfolio to increase your chances of getting a job.

5:58Jon Krohn:Do you have specific guidance for listeners on how many projects they should have or whether they should have projects in kind of different modalities? So I spend a bit more time explaining this to people that are looking for machine learning jobs or data science jobs, which isn't necessarily, you advise a broader group of people. And so when I'm talking about machine learning, I often say things like, maybe you should have one machine vision project, one NLP project. Today in the LLM era, you might actually have a bunch of NLP projects because it's become so ubiquitous. But historically, I'd give those kinds of examples.

6:35Jon Krohn:Oh, and one on tabular data would also be a great idea. Do you have something like that where you would typically say, I recommend that people applying for an entry-level data job should do X number of projects and they should be in X categories? I think there's a couple different ways to approach it. I think the biggest thing for me is when people are choosing projects is to do something that they're actually interested in. Because a lot of times you're building projects, you're going to hit roadblocks. You're doing it on your own time, on weekends, late at nights. Right. And so I think a lot of projects get started and then kind of die because people lack the motivation to finish them.

7:10And so I think choosing a project based off of like a job you're really excited about or a hobby you really enjoy. So, for example, if you're trying to land a job, let's say at Facebook or something at Meta, like I would try to create a project that they would be interested in and they would care about. I think that's like the most important thing. But if like, and even if you just felt like one really killer project for meta, I think that would be enough. But the way I kind of set it up in my accelerator bootcamp is like, okay, more projects doesn't equal bad as long as you're not spending a ton of time.

7:43Right. Cause that's the other thing that people trap that people fall into is like, oh, like once I get like five, you know, I have a SQL project and I have like an ETL project and then I have like a dashboard project. then I'll start applying for jobs. And I think that's a, that's a trap. So my advice is like progress over perfection, like just focus on getting like steps in the right direction and not being a perfectionist. But like, if you can have a project in like multiple different industries, covering multiple different tools, maybe one data viz. If I had to like, if I had to answer this with like a lot more succinctly than I have already, I would say like one SQL project, one data viz project, that alone is a good place to start, but the more the merrier a lot of the time.

8:26Jon Krohn:Nice, perfect. That's exactly the kind of guidance I was looking for. It's a bit trickier for me thinking about these kinds of roles, like a data analyst role, that's perfect. SQL project, a data viz project, that makes perfect sense. That's really cool. Related to what you were saying about people picking projects that they're excited about, you've emphasized in the past in content that you've created that if you're a data professional, you should try to live your life a little data-driven. And so this is related to projects potentially in terms of your portfolio, but also maybe even just kind of more generally.

8:59Jon Krohn:Maybe even to see if you're thinking about transitioning from the lab or whatever you're doing right now into a data career, and you actually haven't spent that much time digging into data, you could find something in your own life. So you, for example, you've talked about analyzing your hikes in Power BI, tracking your dog's steps with a Fitbit. These kinds of ideas, they could tie into somebody's portfolio project directly. You could create a SQL database of your hikes or something and do a data viz of your hikes, any of those kinds of things. That actually sounds pretty cool. You could have some good visualizations of GPS data or something in some kind of geomapping framework in R or Python.

9:45Jon Krohn:You probably don't have people learning R these days. I'm thinking about me getting started in data 20 years ago.

9:54Jon Krohn:I think that's kind of an interesting idea there. You're nodding your head a lot and you've now done a big inhale, so I'm sure you have things to say. Well, it's actually funny because I'm all about personal projects. Like I said, I think the biggest thing when creating a project is actually finishing it and you have to be motivated. I think if you can do something around your life, If you're going to be more motivated to finish it. When I first built my bootcamp in 2021, I actually built it, um, all around, uh, another, I guess, acronym PPP, uh, personal, uh, portfolio projects. And I was like, every, everything I taught was tied back to doing a project in your life.

10:32So like, for instance, when we looked, when we learned dashboarding, uh, I taught, uh, Google looker studio because it was free and I liked it. But anyways, we would create a dashboard of our screen time, like how much time we spent on our phones and like different apps and stuff. And then when I taught Python, I introduced the Spotify API and we analyzed like what music we listened to. And it was super fun. But the problem with personal projects a lot of the time is the data collection can be really hard. It's a lot harder to, especially Apple doesn't want you to know how much time you really spend on your phone.

11:10so they don't let you export it. So you have to like manually do it. So personal projects can be like really meaningful and really cool on a resume, but they can also be more time intensive. So it's a trade-off there, right? Cause like hitting download on a CSV and throwing it in Tableau, that takes a lot less time than like manually doing all this effort. So I'm a huge fan of personal projects. You just have to be careful that like they don't take up your entire life. And then one related note I'll say on that is like, if you're in a role right now that has like nothing to do with data, like just trying to start to think like a data professional, even if you're not even doing any analysis, I think can be really cool.

11:47In one of my last episodes of the Data Career Podcast, I was with one of my students, her name was Jen Hawkins, and she was a delivery driver. And like a delivery driver has to be like one of the least data analyst roles you could have, right? You're not a computer. But she was like, when she had to like get into a gate-coded community or like a gate-coded apartment building, she would have to send text messages to the recipients. And she was thinking, I'm going to A-B test my copy and see like if that like lets me get a higher success of getting in or gets me in faster. There was no analysis, real analysis, like statistically of this data, but that alone I think makes a great bullet point.

12:32And she was so good at this that like people, when they look at her LinkedIn, it looks like she was, you know, a data analyst in this role. She said like A-B testing marketing messages. That was basically her bullet point for this role. She was texting people to deliver packages. But like, if you can have just like an analytical mindset, I think one, that's going to make your job more enjoyable now. But two, it's going to make great bullets for your resume and give you experience, you know.

12:59Jon Krohn:From creating solid portfolios, we move to career success stories. In episode 899, I chat to Kirill Arimenko, who many listeners will know as the founder and the original host of this podcast. In addition to founding this podcast, he's also the founder of the namesake of this podcast, the educational platform SuperDataScience.com. In episode 899, Kirill returned to his podcast to bring us lessons from SuperDataScience.com students who went on to have fantastic careers. In this clip, he details his third example, a Los Angeles-based senior developer.

13:34Kirill Eremenko:Number three, Clara. She's a senior developer living in LA and in her mid-40s, aiming for roles in the 200 ,000 plus salary range. So this person, she has been working already for two decades or more in this space and has tons of experience. In fact, has done all sorts of roles in software engineering, developing apps, developing programs, developing different things for different companies. Most recently for the past, I think it was five years, sorry, three or five years. I don't remember. Let's say three years. She has been creating software using Python. Interestingly, software that processes data, lots of Excel, lots of CSV files using Python in the medical space.

14:34Kirill Eremenko:In fact, a lot of our members, I don't know the exact percentage, but a lot of our members I speak with work in the medical space, supporting companies, whether it's hospitals or pharmaceuticals or other medical space related, like medical equipment companies, procurement companies, or supply chain companies and so on. Anyway, so she's been creating all this software using Python, specifically Pandas and other tools to process lots of data. So lots of Python experience. And recently has done four of our machine learning courses, machine learning A to Z, machine learning level one, machine learning level two, machine learning level three.

15:15Kirill Eremenko:And she wants to get into the space of machine learning and AI. Why? The reason is because Clara is in her mid-40s. She predicts that she'll be in the workforce for at least another 15 years. And she can see that the current role that she's doing is while pays well and she's very good at it, it might not be as relevant in the future. It's not a role that, as we discussed with Clara, It's not a role that's a self-fulfilling prophecy. She's not learning new skills in the role that will open up more doors for her in the future that will keep her growing with the growing trends in technology. She's very selective about applications.

15:58Kirill Eremenko:In fact, she left her job a few months ago to focus specifically on studying and preparing for the new role. She's not in a rush. She wants to take things slowly and basically goes mostly through her network, not applying to thousands of jobs through LinkedIn and so on. Mostly goes through her networks, very selective. And yeah, so that's her goal to get into this space. And the interesting thing, the pain point that Clara has is she's finding there are thousands, literally thousands of job applicants per job. And even at her level of experience, expertise, and background and all these projects that she's done, she's finding it difficult to break in and to land the job that she's looking for.

16:49Jon Krohn:Interesting story. Why do you think that is?

16:53Kirill Eremenko:Why do you think she's having trouble? That's a good question. I think it's probably related to this phenomena where there's lots of jobs, but there's also lots of applicants and it's really hard to stand out. I think it's been the same for the past 10 years when there's lots of people applying through the direct means of just submitting a resume and all of them get pre-screened with AI tools. And if the hiring manager had a conversation with Clara directly, like magically, then they would realize she's amazing and they would hire her in a heartbeat. But because it's really hard to get in front of people this direct way, I think that's the problem.

17:45Kirill Eremenko:And I think Clara has got the right idea of going through connections and going through networking to get in front of the people quicker. What do you think?

17:59Jon Krohn:Networking, ideally in person, is, I think, easily the best way to get your professional opportunities. Not everyone can do that. You might have a family situation or just where you are geographically. If you want to get a job in data science or AI, maybe there aren't in-person things you can be doing. Remote's really the only option. There are probably then in that kind of scenario still things like superdatascience.com, these kinds of platforms where you can get involved, you can do collaborative projects together, get to know people. That gives you that kind of collegial feeling. You'll remember the projects that you've worked on, the people you've been with, their expertises.

18:41That's something like working with someone in an office and understanding what they can do.

18:46Jon Krohn:and maybe they'll open a door for you some years from now. So the more you do that, the more that you're working with people online if you have to. But ideally, you are meeting people in person. There's something about,

19:07Jon Krohn:in the US, there's something called meetup.com. And with meetup.com, you can in any major city in the US or Canada, you can find meetups for whatever you're interested in. It's not specific to tech. I'm sure there's microwave reprogramming chefs meetup. There's all kinds of specific things out there. But in data science in particular, there's lots of different kinds of meetups. And you go and you could be at any stage. You could be just getting started. You could be thinking about it. Maybe you're a medical doctor and you're tired of just dealing with one human at a time and you have a vision for some kind of medical AI system that you want to build to scale up your impact.

19:59Jon Krohn:And so you can start going to these meetups and meeting people and decide, okay, well, maybe how can I take further steps into this? Should I be joining a platform like superdatascience.com or do a master's in person at a local university? So you could be at that very early stage where you're just exploring if a career in data science or AI is something you're interested in, all the way through to being a big expert. You might participate in giving the talks if you're an expert. Often these meetups have that. It could be based around one or two speakers talking about real-world projects or some open-source library they're developing.

20:38Jon Krohn:But around these, you learn stuff from the speakers, but around these you also have lots of social interaction. There's drinks at a lot of these. Pizza is often the food that they order. Sometimes it's sponsored by some local data science or AI company, or maybe there's some small fee like$5 or$10 that you pitch in to be able to buy the pizza and the beer or whatever. It's in those social interactions that you meet people and some people just you click with them and you chat with them more. you see them there a few times. And yeah, you might find your next job. You might find your romantic partner.

21:19Jon Krohn:You might find your best friend. You never know. In a way that I think those kinds of things, those kinds of connections, it's a little bit harder to make them online, but it can happen. I have personally had a lot of good experiences with meetup.com. It's actually been critical to advancing my career. And what's great about these events is that they can happen anywhere in the world. So look out for a meetup that's relevant to you if you're looking to advance your career or even just meet some like-minded people. My final clip is taken from episode 895. In it, I speak to Sean Johnson, co-founder and general partner at AIX Ventures in San Francisco.

21:57Jon Krohn:As an investor in early-stage startups, Sean gave me some great insights into what he's looking for and how he evaluates the market. When you're evaluating these early-stage AI startups that you invest in, What are some of the non-obvious signs of product market fit that you look for? Or I guess even more generally, what are you looking for in those investments? How do you evaluate that the market is ripe for that particular type of AI in that particular application? You know what? Early stage is really a game of people. You back the founder or founders when they're just having their idea. Maybe they have some prototype or some product, but it's really a people bet.

22:44And, you know, they will go out there with their vision to change the world and they will learn quite a lot. And that will result in pivots, you know, micro and macro. And so, you know, we don't we can't say like, you know, I don't I don't think VCs are like genius market timers. Right. I think they can have a sense of that, but then also recognize that the founder will do what they need to do. And we really just look at investing in teams that can execute at sort of the speed of light and pivot however much they need to to find a resonant point between what their offering is in the market. And then get to that great growth trajectory.

23:33Jon Krohn:That makes a lot of sense. But then it begs of me, it begs the same, a similar kind of question, which is how do you then identify that kind of founder or that kind of founding team? I guess, you know, is there kind of, I've had guests, I've had investors in the past who have said that with AI startups, they typically look for this kind of three-legged stool of a CEO, which is somebody who's great at selling the idea, a CTO who obviously is highly technical. But then in an AI startup, you also have this AI expert where the CTO is maybe more concerned with platform scalability, reliability, those kinds of concerns.

24:17Jon Krohn:You have this third co-founder that is the AI expert at or near the cutting edge, like you described Richard Socher or Chris Manning might be, with their research. Does that kind of ring true to you as well in the teams that you're investing in? And not, I would say, 100%. The way we think about it, we start by looking at the team and assessing two factors. One is AI nativeness, right? Do we consider this team to be quite deep in AI or not? And then market savviness or commercial savviness, right? Do they have expertise in this area? Do they have any right building into this market? it. And that's really where we focus.