Jon Krohn: 00:06
This is Episode number 902, our In Case You Missed It in June episode.
00:21
Welcome back to the SuperDataScience 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.
00:51
We framed the common problems that we have in organizations around adoption of AI or any other new technology. You’ve talked about particular solutions, including case studies of situations where you’ve been really successful making a big impact with AI, and you’ve provided some here and there 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 their organization?
Diane Hare: 01:48
Yeah, so I have five.
Jon Krohn: 01:53 Perfect.
Diane Hare: 01:53
The 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. You can either-
Jon Krohn: 02:49
We’re into number two now?
Diane Hare: 02:50
Three.
Jon Krohn: 02:50
Oh, we’re into number three.
Diane Hare: 02:51
So first one, top down, bottoms up. Bold claims and proof points-
Jon Krohn: 02:56
Is number two. Got you.
Diane Hare: 02:57
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 the inspire. So focus on storytelling to tell the broader impact, to describe the ROI in a way that’s inspirational.
Jon Krohn: 03:21
Right. So if you’re being incentivized, there’s something like there’s some end of year bonus that you’re chasing, so you’re not necessarily inherently motivated by some challenge or opportunity. You’re being extrinsically incentivized to go in some direction. But if you’re inspired, then you 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. There’s going to be a big impact in the firm, and it’s a great opportunity for you.
Diane Hare: 04:08
Yeah, the impact you have is bigger than you. 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-
Jon Krohn: 04:25
Number four.
Diane Hare: 04:26
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 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. The way we’ve always done business, if you’re crazy, we can’t change. 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.
Jon Krohn: 05:10
Nice. I didn’t write those down. Can you recap the five for us quickly?
Diane Hare: 05:14
Sure. First one, top down, bottoms up. Second, bold claims and proof points. Third, two levers, inspire or incentivize. Fourth, 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.
Jon Krohn: 05:38
I definitely went away inspired by Diane’s approach, so I guess she got me at 0.3. 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.
05:58
Do you have specific guidance for listeners on how many projects they should have, or whether they should have projects in 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. 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. And one on tabular data would also be a great idea. So 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?
Avery Smith: 06:49
I think there’s a couple of 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. So I think a lot of projects get started and then die, because people lack the motivation to finish them. So I think choosing a project based off of 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, I would try to create a project that they would be interested in and they would care about. I think that’s the most important thing.
07:28
And even if you just built one really killer project for Meta, I think that would be enough. But the way that I set it up in my accelerator boot camp is, more projects doesn’t equal bad, as long as you’re not spending a ton of time. Because that’s the other trap that people fall into is, once I get five, I have a SQL project, and I have an ETL project, and then I have a dashboard project, then I’ll start applying for jobs. And I think that’s a trap. So my advice is, progress over perfection. Just focus on getting steps in the right direction and not being a perfectionist. But if you can have a project in multiple different industries covering multiple different tools, maybe one data vis, if I had to answer this a lot more succinctly than I have already, I would say one SQL project, one data vis project, that alone is a good place to start, but the more the merrier a lot of the time.
Jon Krohn: 08:26
Nice. Perfect. That’s exactly the kind of guidance I was looking for. It’s a bit tricky for me, thinking about these kinds of roles like a data analyst role. That’s perfect. SQL project, a data vis 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 more generally, maybe even to see if you are 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. So these kinds of ideas, they could tie into somebody’s portfolio project directly.
09:26
You could create a SQL database of your hikes or something, and do a data vis of your hikes, any of those kinds of things. That actually sounds like a pretty cool, you could have some good visualizations of GPS data or something, and some kind geomapping 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. So yeah, I think that’s 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.
Avery Smith: 10:02
Well, it’s actually funny, because yeah, I’m all about personal projects. like I said, I think the biggest thing in creating a project is actually finishing it, and you have to be motivated. And I think if you can 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 all around another acronym, PPP, Personal Portfolio Projects. And I was like, everything I taught was tied back to doing a project in your life. So for instance, when we learned dashboarding, I taught Google Looker Studio, because it was free and I liked it. But anyways, we would create a dashboard of our screen time, how much time we spent on our phones, and different apps and stuff. And then when I taught Python, I introduced the Spotify API, and we analyzed what music we listened to.
10:57
And it was super fun, but the problem with personal projects a lot of the time is, the data collection can be really hard. 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 personal projects can be really meaningful, and really cool on a resume, but they can also be more time intensive. So it’s a trade-off there, because hitting download on a CSV and throwing it in Tableau, that takes a lot less time than manually doing all this effort. So I’m a huge fan of personal projects. You just have to be careful that they don’t take up your entire life. 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.
11:47
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. And a delivery driver has to be one of the least data analyst roles you could have, right? You’re not a computer, but she was, when she had to get into a gate-coded community or 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 if that lets me get a higher success of getting in, or gets me in faster. There was no real analysis statistically of this data. But that alone, I think makes a great bullet point.
12:33
And she was so good at this that people, when they look at her LinkedIn, it looks like she was a data analyst in this role. She said A/B testing, marketing messages. That was basically her bullet point for this role. She was texting people to deliver packages. But if you can have just 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.
Jon Krohn: 12:59
From creating solid portfolios, we moved to career success stories in Episode 899, I chat to Kirill Eremenko 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, www.superdatascience.com. In Episode 899, Kirill returned to his podcast to bring us lessons from www.superdatascience.com students who went on to have fantastic careers. In this clip, he details his third example, a Los Angeles-based senior developer.
Kirill Eremenko: 13:34
Number three, Clara. She’s a senior developer living in LA, and in her mid-forties, 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 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, 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 equipment companies, procurement companies, or supply chain companies and so on.
14:55
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 1, Machine Learning Level 2, Machine Learning Level 3, 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 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, while it pays well, and she’s very good at it, it might not be as relevant in the future. 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.
15:56
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. 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 literally thousands of job applicants per job. And even at her level of experience, expertise and background in 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.
Jon Krohn: 16:49
Oh, yeah. Interesting story. Why do you think that is? Why do you think she’s having trouble?
Kirill Eremenko: 16:55
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 ten 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, 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 idea going through connections and going through networking to get in front of the people quicker. What do you think?
Jon Krohn: 17:58
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. And there are probably then in that kind of scenario still things like www.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 expertise. That’s something like working with someone in an office, understanding what they can do, and maybe they’ll open a door for you some years from now. So the more you do that, the more that you are working with people online if you have to. But ideally, you are meeting people in person.
19:03
In the US, there’s something called meetup.com. And with meetup.com, 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 microwave programming, chefs meetup, there’s all kinds of specific things out there. But in data science in particular, there’s lots of different of these 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 like 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. 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 www.superdatascience.com, or do a master’s in person, at a local university?
20:16 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. 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. And sometimes it’s sponsored by some local data science or AI company, or maybe there’s some small fee like five bucks or 10 bucks that you pitch in to be able to buy the pizza and the beer or whatever. And yeah, 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.
21:15
And yeah, you might find your next job, you might find your romantic partner, 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.
21:48
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?
Shaun Johnson: 22:27
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. And 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, micro and macro. So I don’t think VCs are genius market timers. 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 the speed of light, and pivot however much they need to find a resonant point between what their offering is in the market, and then get to that great growth trajectory.
Jon Krohn: 23:34
That makes a lot of sense, but then it begs of me a similar kind of question, which is, how do you then identify that kind of founder or that kind of team? I guess 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 who, 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 or near the cutting edge like you described Richard Socher or Chris Manning might be with their research. Does that ring true to you as well in the teams that you are investing in?
Shaun Johnson: 24:33
Not that I would say a hundred percent. The way we think about it, we start by looking at the team, and assessing two factors. One is AI nativeness. Do we consider this team to be quite deep in AI or not? And then market savviness or commercial savviness. 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 that market landscape is, where will they need to improve? It’s never perfect. You don’t find teams that are always optimally AI native and optimally commercial savvy. 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, et cetera. And then I think when technological inflection points like we’ve seen with ChatGPT happen, what happens in the market is, you have a number of consensus applications that are now possible.
25:43
Everyone knows that we should do AI powered tutoring. 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 as the years go on and you start getting outside of this can consensus driven investing, you go back into the market savvy investing, where you don’t need as much the AI native teams. They’ll still be very good, but it becomes more important to have a market insight that is non-consensus. So the way we think about it, if you think about SaaS investing, let’s call it five years ago, SaaS investing, there wasn’t a ton of differentiation on the MCV stack. It’s not like you’re like, the model technology is unique, the database is unique, or the controller is unique, or the view is unique.
26:51
It’s all Mongo and 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 will be made against what that technology can create. But right now, I think what 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.
Jon Krohn: 27:48
Right. That sounds like a great balance, and I guess I was being oversimplistic in my kind of thinking of, yeah, this is the founding team. It makes perfect sense that obviously every situation is different.
Shaun Johnson: 28:01
Yeah. And the last thing I’d add to that question, Jon, is this comment on, do you need the AI expert on the team? So you have your CEO that’s market savvy, and you have your CTO that’s like a builder, and then do you need this AI expert? And I’ve seen this on lots of teams, even my last team at LILT. 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 a PhD that can just sit there and read papers. Ideally, your folks building in production are also savvy enough to read papers, is our take on it. Good news too, because AI PhDs are expensive.
Jon Krohn: 29:02
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 SuperDataScience Podcast with you very soon.
