ο»ΏD25 Transcript
ββFiona (00:09)
Welcome to unDUBBED where we're unscripted, uncensored, and undeniably data. I'm Fi Crocker.
Sarah (00:16)
And I'm Sarah Burnett and today we're exploring something that's reshaping modern careers, the shift from traditional career paths to strategic career loops and what actually creates value in data roles beyond just collecting more certifications.
Fiona (00:33)
We're joined by Dr. Genevieve Hayes, a data scientist, actuary and PhD level statistician, who's unlocked the secret to transforming technical skills into real business value. She runs her own consulting firm and helps organizations extract value from data, whilst growing their in-house capabilities.
Sarah (00:54)
Genevieve is also the host of podcast Value Driven Data Science, a weekly masterclass on turning data skills into serious clout cash and career freedom. Her show focuses on creating data solutions that bosses can't ignore, bridging the gap between data geeks and decision makers
and helping data professionals transform from technical executors into successful datapreneurs. So if you've ever felt like you're chasing qualifications without seeing the career impact you expected or wondering how to position yourself strategically in the rapidly changing landscape, this conversation's for you.
Fiona (01:33)
Before we dive in, data peeps, you know how this works. Help us get the content algos warmed up. Hit subscribe and share this episode with anyone navigating their own data career journey. Genevieve, welcome to unDUBBED. We're absolutely delighted to have you with us today.
Genevieve Hayes (01:51)
Great to be here, Sarah and Fiona.
Fiona (01:54)
Yeah, it's awesome. also really nice to have someone from Australia as well. We always love starting with the person behind the profession. Could you give us a short snapshot of your journey? Just the key steps that brought you to where you are today.
Genevieve Hayes (02:09)
Okay, so I'm an actuary and statistician turned data scientist turned datapreneur, I guess. So I originally trained as an actuary and then went and did a PhD in statistics. And I thought that that was going to be my forever job. So I imagined being
in a university until the day I retired and because academics last forever, possibly until I died. and it was great. And look, I love my time as an academic, but I got to the end of my PhD and realized I don't actually want to spend the rest of my life in school. So I thought, okay, now's the time to actually do something different. And that was my first career pivot.
So I decided, I'm going to give up on academia and try and go into industry. And I got a job in insurance pricing and analytics and did that for a number of years. Then it was around 2015 and I started to hear about this great, exciting thing called data science and machine learning. And
I was managing a team at the time and I was looking for learning and development opportunities for members of my team. So I started to look into it a bit and realised this is actually something that I want to do. So rather than creating learning and development opportunities for my team, which I did also do, I started creating them for myself and I started doing a master's in computer science. in machine learning.
And I also convinced my boss to allow me to expand the remit of my role so that I wasn't just managing a actuarial and business intelligence team. We became the actuarial and data science team. And so that was how I got into data science. So that was the next career pivot. And that led to me ultimately after that job, I went on and took a technical specialist role.
as a data scientist in a big government organization. And then COVID hit. And as you know, we Australians spent a lot of time locked up during COVID. So I had a lot of time to rethink what I was going to do with my life. And I wasn't really interested in pursuing a career in
executive management or something like that. So it occurred to me that if I kept doing what I was doing, it was going to become a bit like Groundhog Day. So I thought, What do I actually want to do? And I spoke to a lot of people about what they did and turned out that the ones that excited me most were the ones who were doing their own thing. So I thought, okay, I've got a bit of money saved up.
from the COVID lockdowns, let's use that to bankroll a new venture, which was my own consultancy work, which is the short version of how I got to where I am today.
Fiona (05:27)
That's amazing. What I really love at each point you're going through things, you've really tackled the problem solving to figure out how to get to the next step. One thing that I just want to bring you back to was how you mentioned that you expanded your role because I feel like a lot of our listeners often work in corporate environments where they feel like they've got more to give.
but they feel that the only opportunity is to go somewhere else. And I really like how you wanted to change your role so that it included data science as well. So can you just pause for a moment and walk us through what that conversation actually looked like with your leaders to get that across the line?
Genevieve Hayes (06:13)
when I first wanted to go into data science, that was my original thought. You know, why not just go off and find a new job? That's exactly what I was looking for. But the thing was it was 2015. So a lot of those jobs just didn't exist in Melbourne, And also I liked my job.
I didn't really want to move somewhere else. So for me, this seemed like the best fit. And what I could say back then was that this was going to be the way of the future.
especially for things like insurance and insurance pricing. My organization was going through a restructure at that time. they were actually looking at what would the business look like five years from now. So they were open to ideas.
And I put together a business case and I said, okay, I see this as the way of the future because of and listed the reasons. I gave some examples of how it had succeeded in other organisations that I'd researched. And I actually went around and spoke to a lot of people who were doing it at places like the Australian Taxation Office. So I was able to make that business case. And then I said,
Here are the problems that you as an organisation are facing. Here is how data science could solve those problems. And my team is the best place to do it because we already have these skills and here's how we can restructure the team so that we can bring in the skills that we don't have. I spoke at a lot of executive meetings and they approved it. So.
that was how I became the first data science manager in that organization.
Sarah (08:03)
Wow, that's really amazing. And I really love the way that you basically did this whole campaign pitch internally. And, like you said, interviewed a lot of external people and then also spoke a lot with stakeholders and the business and so forth. Were there any hurdles along the way that maybe you could give as advice to people that are thinking about doing something similar?
Genevieve Hayes (08:29)
Well, was basically a matter of answering the three consultancy questions. Why this? Why now? Why me? And by addressing all three of those questions, I was actually able to preemptively overcome any hurdles that I think I would have faced. But I think if you are planning on pitching any services that you have or any ideas that you have, those are the three questions that you're going to hit hurdles with if you can't answer.
Sarah (09:00)
it's such a big step to make and I love hearing success stories When the transition was approved and everything, then what did it look like going forward because you've already got an established team, I guess getting them on that journey as well.
Genevieve Hayes (09:17)
Well, with my team, it was a two-pronged β transition. So there was one prong of it was training the existing staff and helping them to move into data science. And the other, the second prong was recruiting new staff. During this period where I was promoting it, we'd recently had one staff member leave and
we actually did have budget to recruit an additional staff member on top of that. So I'd actually held off on recruiting both of those roles, which allowed me to bring in two new resources that were dedicated data science resources. So that helped. I was actually also able to lobby with the organisation so that we could introduce a internship type programme. So
where we could actually bring in a student so we could get additional resources like that. we had, it was a team of six people plus me. One third of the team was brand new because of this transition. So straight up one third was already always going to be on board. With the others, two of them were actuarial. So they weren't as affected as the others. And then there were two that were in the middle and they were actually quite
enthusiastic about this because for them it was a matter of they got the opportunity to build their skills and they could see that they were going to become more valuable in what they did. So as a matter of pitching it to them as this is going to help your career and whether you choose to stay here or go elsewhere this is going to make you more valuable to whoever's employing you.
Fiona (11:17)
That's fascinating. I'm coming back to the why this why now why me because I really love that as a statement. It's really clean and easy to follow. Do you have any other nuggets of wisdom to offer us in terms of your approach of creating value as a data professional? So what does that actually look like in practice?
Genevieve Hayes (11:41)
So when we first started doing this, I was really excited about data science and all these new machine learning things that I was learning.
And so were the new data science recruits in my team. And one of the mistakes we made initially was doing a lot of proof of concepts that were just showing off, Hey, look, we can do this really cool thing where we can fit this model. Yay. do you have any other models that you'd like us to build? And everyone's like, yeah, this is really great. What do we with it? And after that initial
setback. What we realized was data science isn't just about building toy models, it's about solving problems. so what we started doing in this role and what I did in subsequent data science roles was start to look at what are the problems that the stakeholders are facing.
What are the problems that are identified in the β strategic plan of the organization? Or in another organization that I worked at after this, had β within the team that we were looking in, that we were supporting as data scientists, what were the problems that that team was facing? Which we knew about because they were shouting out for help with these problems.
And then it was a matter of creating a solution to solve those problems. So you're basically treating yourself and your team, not as commodity resources, but actually acting as an in-house consultancy or as a business within a business. And if you take that approach, then everything works a whole lot better than if you're just showing off skills.
Sarah (13:39)
I really like the approach that you've taken and making sure that it does add value.
to the business at the end and then obviously trading as a business within a business and almost being that mini consultancy that can go around and help project by project what other pieces do you find that really helps you become that strategic contributor?
within the organization.
Genevieve Hayes (14:07)
I see there are three main elements to becoming a strategic contributor. One of them is focusing on solving the problems. it's opportunity identification and creating solutions that solve those problems. The second element, which isn't so much strategic as technical, but it's critical to actually becoming valuable to the organisation, is being able to deliver those solutions.
in a effective and efficient way. So another big problem that data scientists often face is that they get the problem or they define the problem themselves and then they vanish for six, 12 months until they get the solution. By which point people have either stopped caring or forgotten or the problem's been solved in some other way. So
there's that whole element of rapid iteration. So making sure that you're delivering value consistently to your stakeholders and getting feedback from them to make sure that you remain on track.
Then the third element is both measuring the effectiveness of your contribution and being able to communicate that back to management.
One of the biggest problems you can have as a data scientist or as a data science team is if you're producing something that people recognize as being valuable, they don't consciously recognize it as being valuable. Sort of like clean water that comes out of our taps. So I think we all recognize that there is a value to having clean drinking water, but no one really thinks about what goes into that at the water authorities.
So you don't want to become like a water authority where people value your contribution, but they're not consciously connecting your contribution to the result. So there's that whole element of being able to say to management or senior stakeholders, we produced solution. This created this value to this organization, which solved this business problem for you.
And by making that unconscious value conscious, then you're creating a momentum so that you can get approval for bigger and better projects.
Sarah (16:41)
Hmm.
Fiona (16:42)
It's absolutely one of the things that I think that people who work in data and analytics fail to do repeatedly, which is celebrate the successes and have that moment of pause for reflection on how well did my program actually go? What was the impact? Because often we're obsessed with getting stuck into the next opportunity and actually solving that problem
and also communicating that effectiveness out because it's a big thing to keep reinforcing the behaviors or reinforcing the successes. Otherwise, you know, it's really easy for data teams to become another line on the spreadsheet in terms of here's their budget. This is how much they cost them. Often the roles are more expensive than other roles as well because of the technical expertise that
the staff members actually bring along. So I love that you're bringing up celebrating those wins and doing that reflection and communicating it out there and reinforcing it with the leaders as well. Stepping back into the first step in that process though, how do you approach identifying opportunities that other people might miss, particularly in emerging fields such as data science?
Genevieve Hayes (18:04)
Well, the strategy that I use is start with quick win projects. So you don't want to be pitching some massive hundred thousand or million dollar project that's going to take 12 months or two years to deliver with a cast of thousands involved. Because if you have no evidence to prove that's going to succeed, you're probably not going to get approval for that to begin with. And if
By some miracle you do get approval and it fails, it's going to look really, really bad. What you want to do is if you see something, you know, a problem, look for a way you can create a quick win solution in less than a month, say, and then look at how that quick win solution performs. If you're seeing early signs of success and traction with that, then that's a sign that you want to keep going down that path.
and exploring that path sooner. But if that project either fails or turns out to be a non-starter or people just go, yeah, that's great and don't really care, then that's probably something you don't want to invest your time in. And this is a case with projects and with anything in your career. Just take early steps, experiment, get the feedback and then scale what works.
Sarah (19:09)
you
those are some really key skills that is great to evolve. You've also got a skill around seeing what clients actually need versus what they think they want. How have you developed that over time?
Genevieve Hayes (19:49)
Well, that was one of those skills that I developed β from making the mistake of delivering to clients what they said that they wanted and then discovering that it wasn't what they actually needed. So it was basically an iterative process. know, the first time when I was early in my career, a client would say to me, I would like this and I would do it because my background was in insurance pricing. And if someone says to you, okay, we'd like a set of
Fiona (19:58)
Yeah.
Genevieve Hayes (20:18)
premiums for this particular product, they generally did actually want or need a set of premiums for a particular product. So you didn't have that want/need disparity in insurance pricing. And so I just trusted people initially when I started going into data science. And first time that happened, I thought, okay, it's just the stakeholder that I'm working with. She's just being difficult. But then it happened again a couple of other times and I'm like, okay,
This is probably not just them. It's probably me. So I started looking at, okay, well, what questions could I have asked these people so that things would have gone better? And so started, every time I'd run into a problem where someone would say they'd want something and they'd need something else, I'd start creating questions to address the problems that I'd ended up facing because I trusted them too much.
And then through that, I started coming up with a set of questions. And every time I got through that, what I was delivering to them got closer to what they actually needed. And then I don't think in the discovery phase it's ever possible to fully understand what someone truly needs. So then...
Because I kept on running into those problems at the end, I started to introduce that iteration process. So talking to them throughout the process and gauging feedback on, know, we've done this so far. What do you think of it? And that initially just took the form of, you know, regular coffee catch-ups with them. Initially, I think I did it once every week or fortnight, then I built on that and became more frequent.
Eventually I ended up with a process that delivered results where people were actually happy with what I delivered to them and it met their needs rather than just addressing their initial off the top of their head wants.
Sarah (22:27)
And I think that's really important to understand that, projects, goals, everything evolves as the process just evolves. And if you think you're gonna, develop something that's at the beginning is the same as, at the end without looking at what the results have been throughout that period, then you're probably not gonna deliver to the needs anyway.
Genevieve Hayes (22:50)
And it's not that people are being difficult. If you're talking to something like Claude or ChatGPT, you can actually see it in your interactions with these AIs. You'll ask for something, the chat bot will give you exactly what you've asked for, and then you go, hang on, that's not even what I want. What I really want is something. And so you end up with this iterative conversation. So I find that that's been really enlightening for me because it's allowed me to put myself in the
shoes of the stakeholder and say, yeah, I'm not trying to be difficult to this chat bot, but what I thought I wanted wasn't actually what I wanted once I saw that response. And so yeah, you can take that sort of process and apply it to your work.
Fiona (23:38)
It really sounds to me like you're a lifelong learner. Not only are you doing your PhDs, your masters, everything and keep coming back, you do a lot of self-reflection What's the thing that you're working on for yourself right now that you've realized that you would like to improve to help your career?
Genevieve Hayes (23:58)
That one was actually one of those things that came out of a conversation that I was having with my mom. So I read a lot of those, I guess you'd call them airport business books. And I'm actually reading Hidden Potential by Adam Grant right now. And one of the things that I was discussing with my mom,
is how some of the examples in this and in other books that I've read, they tend to focus on two groups of people. They're either people who already have privileges. So they're from very good backgrounds, have parents who are already successful, went to good schools, things like that. Or they're people who won the lottery, the genetic lottery, like the Olympic level
athletes or something like that. And one of the comments my mum said to me was that she always sees herself as being an ordinary person and nothing is ever done for ordinary people. You know, she's, she was saying, you know, what about people like me in the middle? I want to do well too, but everything's designed for people with, you know, extraordinary privileges or extraordinary luck in their genetics.
And so the challenge that I set myself after that conversation is, how can I create a system that can help ordinary data scientists succeed? You know, not the one that's going to get the job working to design the next chat GPT, but just your ordinary hardworking data scientists who didn't come from a privileged background, isn't
Gregory Hinton or something like that, but that will work for everyone. And I'm really enjoying it because
creating a system that's a whole lot more robust. And yeah, so that's one of the things that I'm working on at the moment.
Fiona (26:01)
it resonates a lot with me and in my past environments and the last three corporate organizations I worked at, I designed different learning programs specifically to help people transition from being Excel analysts into data visualization specialists or
even data engineers using different forms of tooling and gamification programs as well. So I have a strong belief that people don't need to go to university to be quite skilled in analytics through different ways of learning. So micro credentialing, for instance.
Genevieve Hayes (26:41)
I am actually a strong believer in microcredentialing.
I am one of those people who I chase university degrees as my solution to a lot of things, which is something that you can see in my resume. But I don't believe that that's actually the best approach to succeed in data science. I think it gets your foot in the door, certainly, but I don't think that that necessarily is what
is going to create value within organisations. I think if you just keep chasing degree after degree, you're actually going to hit a ceiling because
you're letting your credentials speak for you rather than the value that you bring speak for you.
Fiona (27:26)
100%. And I think that the interesting thing about micro credentials to me is with a university degree, there's filler, that you need to be passing along the way in order to get the overall degree at the end. Whereas with micro credentialing, you can take a look at a specific subject area and go deep on it and go hard after it.
Genevieve Hayes (27:52)
Well, I think the thing I like about microcredentialing is that you can learn the skills that you need on an as needs basis. So I think it's good to have a foundational degree. Some of the things that I learned that have really set me up well is just doing first year economics, for example. I think it is really helpful to have an understanding of how the economy works. I think it's really helpful to understand the basics of accounting.
things like that because just having that foundational knowledge is going to make you more effective in everything you do. And the same goes for things like coding statistics, stuff like that. So I think there is a place to get your initial qualifications, but I don't think you should just keep spending the whole of your career going after qualification after qualification. I think once you've covered off all those foundational skills, you are actually better off
then seeking out problems to solve within a business and then looking at what can you do with the skills that you have and then what skills do you need in addition to those skills in order to solve those problems at hand. And that's where micro credentialing I think becomes very useful because you can say, well, I'm for example, dealing with a computer vision problem. I don't know anything about computer vision.
Can I go and do a one semester long micro credential and learn those skills and then apply them to that particular problem? And I think, I think that works very well. So I think that's better than doing a full blown $50,000 masters just because you want to solve one problem. mean, that's getting a bazooka to sort of swat a fly type thing.
Sarah (29:48)
And for our listeners, what's some practical steps that you would recommend in terms of finding these micro-credentials?
Genevieve Hayes (29:58)
Well, you can just start with MOOCs. When I was starting to learn about data science, one of the first things I knew was that my coding wasn't that great. So I just took a, 12 hour online course on how to program in Python. And that just gave me foundational skills that I could then use and build on.
Just a Google search or a search on platforms like Udemy, β Udacity, Coursera, things like that are enough to find those. And you can probably do it for free and that'll just get you up and running. Or just look for YouTube videos on the topic.
Fiona (30:36)
Yeah, we live in an incredible age where there's so much information that's available. I know certainly from some of the things that we have to do for running DubDub, for instance, even around the web development side, it's like, β I really like to have a button that subscribes people from my blog onto my YouTube. How do I go about that? And, you know, within 10 minutes of prompting and trying a few things.
ChapGPT has helped me find what the code is to actually go and develop that. And it's incredible the speed and pace at which we can apply just a general wonder and actually make it a reality these days.
Genevieve Hayes (31:22)
now whenever I get stuck with anything technical with my website, I just put it into Claude or perplexity and ask for instructions. And I'll actually say, I'm using this particular piece of software. What do I need to do? And it'll come back with, you know, click on this button and then click on that button. And it might have some problems with it, but I'll, I'll usually have it solved within the afternoon.
Fiona (31:45)
For sure. So on that vein in the AI space, how should data professionals be thinking about positioning themselves for opportunities that perhaps don't even exist yet?
Genevieve Hayes (32:00)
Well, I think one of the things that you should do is look for early signs of success. if we look at something like a chat GPT and this generative AI, when chat GPT was first released, there were early signs of success with that right from day one. So, and if you compare it with other β internet chat bots,
The ones before ChatGPT, some of those were epic fails. Yeah, you had them becoming racist, sexist monsters within three days and people took pleasure in trying to corrupt these chat bots. and that was what I initially thought was going to happen with ChatGPT. I was just waiting for ChatGPT to start spouting terrible things and get taken down.
When after about two weeks, ChatGPT didn't get taken down for being a horrible artificial intelligence. That was when I started to look at it because it's like, okay, they've actually overcome what was always the hurdle to getting any of those chatbots up and running in the past. And even though people were just doing stupid things with it, like, you know, write me a
poem about insurance in the vein of Dr. Seuss. It was getting traction right, right from that beginning. So you look for those small signs of success in something and those are things that have a high potential to start growing into something bigger down the line. And then just start looking at, you know, what are some quick win projects I can do with this to test this further?
And then what can I do to build on those? So that's how I would go about identifying opportunities that don't exist yet. And then look at with regard to how to position yourself. What you want to do is once you start to see those early signs of success, you want to start looking at what are the problems that technology or whatever it is can solve and how can you
take that and position yourself as the best solution to these future anticipated problems. And that was what I did when I β created that data science team. I could see that data science was showing those early signs of success and this was something that my organisation was going to need probably within the next five years.
If I didn't do it, they would have ended up creating that role anyway, β and then advertising it and throwing it out to the world to apply for. What I did was I positioned myself so that I was the best answer to that problem. And by showing how this could be used, by getting some foundational skills in data science, which I did by taking MOOCs at that time.
No one from outside that organization could beat me with regard to knowledge of the organization. So when they created that role, rather than advertising it, they looked to me and that's what you want to do. Position yourself as the best solution to an anticipated future problem.
Sarah (35:29)
You know, the thing that is overwhelming when I hear you talk is just the immense amount of curiosity that you have and this real thirst for knowledge. it really shines through and, and, know, just listening to you, you talk, which is amazing to hear. β We keep hearing about careers becoming more like loops rather than linear progressions, especially with what you've spoken about with AI and changing the landscape.
What are you observing in this space?
Genevieve Hayes (36:02)
think regardless of what you do, there's always going to be an evolution of your role. And even if you are something as fundamental as an accountant, an organisation is always going to need accountants. But what an accountant does now is very different from what an accountant did 100 years ago.
And so whether your role has changed to the point of changing names and becoming something completely different, or it's just evolved to from being an accountant as it was back in the paper and pen days to being an accountant now with accounting software like Xero and things like that, you're always going to have to keep up with
the latest trends in your organisation, in your career. And those are the loops. Sometimes those loops have different names, sometimes those loops are just the next stage of evolution of your career path.
Fiona (37:13)
Yeah, interesting. I think you're right. There is always an evolution of your role.
I think one thing that struck me later in my career to where I have been previously, perhaps job hopping a little bit more was the importance of establishing myself and grounding myself and having those relationship networks within the organization to help me to be successful and to embed things more.
What role do you think that communication and translation between technical and business stakeholders plays in someone's career advancement?
Genevieve Hayes (37:55)
Well, that was how I got my first management job. So I came out of academia, I'd finished my PhD and I hadn't had years of experience doing those technical roles that β people would have been doing in that same time that I was doing my PhD. And I was in this awkward position where I didn't have that experience in a
in an industry β job, but I didn't want to go into an entry level role because I was in my mid twenties by then and I'd already been leading a lecture theatre. I was a full blown lecturer by that point. And I actually managed to get a management job because I was able to say my whole career as a lecturer was about taking
highly technical concepts and translating them into simple language so that people who previously hadn't β come across those concepts could understand. So I was able to draw that parallel between teaching a class of students and communicating to a room full of executives. And I was then able to demonstrate that within my job interview.
And that allowed me to leapfrog over several levels and become a manager quite early in my career. And from then on, every job I've had has hinged on that ability to take technical concepts and translate them into language that a non-technical audience can understand.
Fiona (39:33)
Yeah, and that's shown straight through as you've been talking with us today is as you've been processing what our questions have been, I can see how you break everything down into simple, easy to digest steps for people and communicate that through. So I think it's a really good demonstration of exactly what you're saying.
Sarah (39:58)
What advice would you give to our listeners if they're considering a major career pivot?
Genevieve Hayes (40:06)
Well.
It's what I've said before, just try it in a small way. So if you're thinking about...
I don't know, going into data science, for example, when I went into data science, didn't, hadn't left my career. I could have gone back to actuarial if that had failed because I hadn't left that job. By expanding the reach of my team into actuarial and data science team, I was able to test that out. So look for ways that you can test out your new pivot in a way that allows you to.
still back out if it turns out to be a disaster. It's sort of like if you're thinking about moving to a different country, you know, don't just sell your house and move to a different country. You could rent an Airbnb in that country for a month and see whether you like it. Come up with your equivalent of renting an Airbnb.
Sarah (41:09)
And I think to your point as well, if you can patch it to your company in a way you have the best of both worlds, right? You kept your original job, you progressed in something that you were more interested in and you managed to balance that out. So I really liked that idea of almost taking little steps at a time and seeing if you can do it where you already are.
Genevieve Hayes (41:18)
Hmm.
And you want to get the experience in whatever you want to move into in the job you're currently in. One of the things that everyone will look at when they're looking at your resume is do you have actual on the job experience in whatever it is that the job is? It trumps any credentials every day.
You only look at people who might have qualifications but no experience if there's no one who has the relevant experience. So get it on the job if you can.
Fiona (42:06)
switching gears a little bit, just still in the data career space though, what do you see as the biggest career development mistakes that people make in the data space?
Genevieve Hayes (42:19)
focusing too much on technical skills. most people who are already in the data space already have most of the technical skills that they need. They might need to fill in some gaps, which they can do using micro-credentials as we were discussing earlier. But there's that whole Pokemon got to catch them all mentality that you see with a lot of data scientists. And look, I've done that myself.
For fun during the COVID lockdowns, I decided to see how many of these cloud certifications I could get. And I don't think that did any benefit to my career whatsoever. Once you get to β the point where you've got your foundational skills, start looking at how you can build your business skills instead, because those are the ones that are going to take you to the next level.
Sarah (43:10)
I think it's shining through now more than ever just around those soft skills as well as, you know, technical skills seem to be very front of mind and a lot of AI supporting those technical skills. think the bigger gap is really in those soft skills and being able to talk business when required rather than talking technical all the time.
Genevieve Hayes (43:15)
Yeah.
Yeah, well, the technical skills are the ones that the AI can automate. lot of those things, like for example, fitting machine learning models, AutoML can do a lot of that work now. And, know, I've heard of, they say in a lot of these things that the jobs that were traditionally done by graduates will now be taken over by AI in the future.
And those tended to be the purely technical roles. You have to start taking more of a entrepreneurial approach, even if you're working within an organization.
Sarah (44:12)
And I think, and leaning into AI doing those roles, you know, doing parts of your role that you no longer need to focus on.
Genevieve Hayes (44:23)
Hmm.
Fiona (44:26)
Well, what an interesting conversation this has been and we could just go on for hours and hours, but we do have a hard stop. So let's move into the rapid fire round. Genevieve, quick fire questions, starting out with what's one myth about career advancement and data that needs busting?
Genevieve Hayes (44:49)
getting more credentials is going to accelerate your career trajectory.
Sarah (44:56)
Nice. Complete this sentence. The most valuable data professionals are the ones who...
Genevieve Hayes (45:05)
understand the business problems.
Fiona (45:08)
I love that. One piece of advice for someone feeling stuck in their current role.
Genevieve Hayes (45:16)
Go and have more conversations with people.
Sarah (45:19)
so important.
Fiona (45:21)
that's such a tough one, I think, for some data professionals is actually having those conversations, you know, and saying, can we can we set up 20 minutes and have a bit of a coffee chat, even if it's just a virtual coffee chat, because the amount of times that I know that I've helped other people because I've connected the dots of what they're trying to do when opportunities float up, you know, you want to take someone who's got a real passion for getting into that space. So I think those conversations are
definitely something that can help out.
Genevieve Hayes (45:54)
And if you're feeling nervous about approaching people about conversations, don't do it formally in the form of meetings. Just have conversations when you bump into them in the corridor or in the lunch room or something like that, because that takes a lot of the pressure off because you're not formally sitting down with them. You're just talking while you're waiting for your food to cook or something.
Fiona (46:16)
really great advice.
Sarah (46:18)
What's something you wish you'd known earlier in your career?
Genevieve Hayes (46:25)
People care more about the value that you bring rather than anything that's written on your resume.
Fiona (46:32)
Wow Genevieve, this has been such an insightful conversation about rethinking how we approach careers in the data space. But before we wrap up, what's the one thing you really want our audience to take away from this discussion?
Genevieve Hayes (46:48)
I think everyone has it in them to be valuable as a data scientist. It's not about who has the fanciest credentials or where you're working. What comes down to the end of the day is can you connect with people, understand their problems and create solutions? And everyone can do that. Everyone can be a superstar data scientist and
everyone can take control of their career trajectory and use it to create the life that they want for themselves, the impact they want within their business and whatever change they want to see within the world.
Sarah (47:34)
that's really lovely. Thank you for sharing that. And Genevieve, where can people connect with you and learn more about your work?
Genevieve Hayes (47:42)
the easiest place to find me is on LinkedIn. And also I have a website, genevievehays.com.
Fiona (47:49)
The shift from qualification collecting to strategic value creation really does feel like a career game changer. Thank you so much for sharing these brilliant insights with us today.
Sarah (48:03)
If you've enjoyed today's episode, please like, subscribe and share this with anyone ready to move beyond certification treadmill to a real career impact.
Fiona (48:15)
And until next time, this has been unDUBBED, where we are unscripted, uncensored, and undeniably data. Thanks for joining us.
