902: In Case You Missed It in June 2025
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This is episode number 902, our In Case You Missed It in June episode. 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.

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'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 thinking

And more effectively enable change in their organization to more effectively be able to, you know, not get stuck on the what and and and and be able to accelerate change and success in the organization. Yeah. So I have five questions.

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. So you need to make sure you're expressing yourself so all different populations can believe and understand you.

The other one is what I said. You have two levers to pull when you're trying to drive change. 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. 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 year bonus that you're chasing. And so, you know, you're, you're, 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, uh,

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. You'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. Number four, 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, 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 are going to have to put yourself into a vulnerable, courageous space and just know that going into it. Nice. 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. 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.

Do you have specific guidance for listeners on how many projects they should have or

whether they should have projects in different modalities. 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.

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. 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. And so I think a lot of projects get started and then kind of die because people lack the motivation to finish them.

Um, and so I think choosing a project based off of like a job you're really excited about, uh, or a hobby you really enjoy. So for example, if you're trying to land a job, let's say, uh, at, at Facebook or something, I've met a, 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. Um, but

But even if you just built 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, right? Because that's the other thing that people trap that people fall into is like, oh, 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 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,

more succinctly than I have already. I would say like one SQL project, one data viz project, like that alone is a good place to start. But the more the merrier a lot of the time.

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. This is related to projects potentially in terms of your portfolio but also maybe even just more generally. 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 that, so you, for example, you've talked about analyzing your hikes in Power BI, tracking your dog's steps with a Fitbit. And so these kinds of ideas, they could be

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 geo-mapping framework in R or Python. You probably don't have people learning R these days. I'm thinking about me getting started in data 20 years ago. And

So yeah. So I think that's kind of an interesting idea there. Do you have any other, I see 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 yeah, I'm all about personal projects. Like I think if you can make, like I said, I think the biggest thing when creating a project is actually finishing it and you have to be motivated. And I think if you can like do something around your life, 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. So 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, um, anyways, we would, 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 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. So they don't let you export it. So you have to manually do it. So

uh, 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, of personal projects. Uh, you just have to be careful that like they don't take up your entire life. Uh,

And then one related note I'll say on that is if you're in a role right now that has nothing to do with data, 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. In one of my last episodes of the Data Career Podcast, it was with one of my students. Her name was Jen Hawkins, and she was a delivery driver.

Um, and, uh, 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 commode, uh, coded apartment building, she would have to send text messages to the recipients. And she was thinking, I'm going to AB test my copy and see like, if that like, this

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. And 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 AB 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, but two, uh, it's going to make great bullets for your resume and give you experience, you know, from creating solid portfolios. We moved to career success stories in episode eight, nine, nine. I chat to Kirill Aramenko, 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. 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

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. 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. And she wants to get into the space of machine learning and AI. Why? The reason is because Clara is in her mid forties. She can, 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 she's

you know, 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, you know,

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. 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.

Um, and, uh, yeah, so that's, 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, um, you know, background and all these projects that she's done, she's finding it difficult to, um,

break in and to land the job that she's looking for. Interesting story. Why do you think that is? 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. If the hiring manager had a conversation with Clara directly, 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. And I think Clara's got the right idea of going through connections and going through networking to get in front of the people quicker.

What do you think? Networking, ideally in person, is, I think, easily accessible.

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. And 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 collegial feeling. You'll remember the projects you've worked on, the people you've been with, their expertises. That's something like working with someone in an office and understanding what they can do. Maybe they'll open a door for you some years from now. 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, 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 like, you know, microwave reprogramming chefs meetup. There's all kinds of specific things out there. But, you know, in data science in particular, you know, there's lots of different of these kinds of meetups and you go and you could be at any stage, you know, you could be just getting started. You could be thinking about, you know, maybe you're like a medical doctor and you're

you're tired of just, you know, dealing with one human at a time and you have a vision for some kind of like medical AI system that you want to build, um, to like scale up your impact. And so you can start going to these meetups and, and meeting people and decide, okay, well maybe like how, you know, how can I take further steps into this? Um, you know, should I be joining a platform like superdatascience.com or like do a master's in person at a local university? Um,

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. 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.

And, you know, sometimes it's sponsored by some local data science or AI company, or maybe there's like some small fee, like five bucks or 10 bucks that you pitch in to be able to, you know, buy the pizza and the beer or whatever. And yeah, it's in those social interactions that you, yeah, you meet people and some people just, you know, you click with them and you chat with them more. You see them there a few times and

And, yeah, you might find your next job. You might find your romantic partner. You might find your best friend. You never know. You know, in a way that I think, you know, those kinds of things, those kinds of connections, they don't, 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. 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 when they're just, you know, or founders when they're just having their

you know, idea, you know, maybe they have some prototype or some product, but it's really a people bet. And, 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, you know, 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 they're offering is in the market and then and then, you know, get to that great growth trajectory.

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 on 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 who, you know, where the CTO is maybe more concerned with platform scalability, reliability, those kinds of concerns. You have this third co-founder that is the AI expert at,

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? 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

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? And that's really where we focus. And then we ask ourselves, given a team and given where we think

kind of that market landscape is, where will they need to improve, right? Like you, it's never perfect. You don't find teams that are always optimally AI native and optimally commercial savvy. And so if you invest in a team that's more AI native and less commercial savvy, then the question is, how do you de-risk the commercial savviness with the team? Maybe that's advisors, you know, et cetera. And then, you know, I think when technological inflection points like we've seen with ChatGPT happen, what, what, what,

what the market... What happens in the market is you have a number of consensus applications that are now possible, right? Like, everyone knows that we should do, like, AI-powered tutoring. And so...

Everybody is like, well, let's build AI powered tutoring. But what you need to do there, we think, is invest in extreme AI native teams that can actually bring experiences to consumers that other teams just cannot. And

And as the years go on and you start getting outside of this consensus driven investing, you go back into like the market savvy investing where you don't need as much the AI native teams. Like this will be very good, but it becomes more important to have a market insight that is

is non-consensus. And so, you know, the way we think about it, if you think about SaaS investing, let's call it five years ago, SaaS investing, you know, nobody, like there wasn't a ton of differentiation on like the MCV stack. It's not like you're like, oh, right. Like the model is like, you know, the model technology is unique. The database is unique or like the controller is unique or the view is unique. It's all, you know, Mongo and right. Like

MySQL and it's in the middle where it's, let's call it Node.js or Ruby and then it's React or HTML, CSS, JS.

And that's all commodity, right? It's just like, well, what's the idea? How are you going to configure it? And AI will get there within the current framework of the technology. Now, if we have a new architecture come out that does replace transformers, then game on again, right? Now, a whole new set of consensus bets will be made against what that technology can create.

create. But right now, I think we're transitioning from you really need AI native teams in a consensus world to you're going to start needing more market savvy teams in a non-consensus world. Right. That sounds like a great balance. And I guess I was being oversimplistic in my kind of thinking of like, yeah, this is the kind of founding team. It makes perfect sense that obviously every situation is different.

Yeah. And the last thing I'd add to that, that question, John, is this comment on like, do you need the AI expert on the team? Right. So you have your CEO that's like, you know, market savvy and you have your CTO that's like builder. And then do you need this AI expert? And, you know, I've seen this on lots of teams, even my last team at Lilt. I think the I think.

The best way to orient a team is to have AI engineers, folks that can build very adeptly with the technology in production that are also savvy enough to be reading the papers and understanding how the technology is changing and be able to integrate that into the stack.

I don't think you need like a PhD that can just sit there and read papers. Ideally, your folks building in production can all are also savvy enough to read papers is is kind of our take on it. Good news, too, because AI PhDs are expensive.

All right, that's it for today's In Case You Missed It episode. To be sure not to miss any of our exciting upcoming episodes, subscribe to this podcast if you aren't already. But most importantly, I hope you'll just keep on listening. Until next time, keep on rocking it out there. And I'm looking forward to enjoying another round of the Super Data Science Podcast with you very soon.