Doctor turned founder: How entrepreneurship can cure cancer | Dr. Padman Vamadevan, Founder of Astron Health

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In this week's episode, Stephanie Melodia interviews Padman Vamadevan is the Co-Founder & Chief Medical Officer of Astron Health, a platform for personalised cancer treatments. Astron uses molecular insights and advanced research to develop a unique algorithmic approach to give cancer patients more targeted treatment options. This tool helps cancer patients and care teams to not only get proactive, long term cancer care but to also extend remission and stay ahead of recurrence. Learn more about Astron Health here.

Watch on YouTube via the link below or keep reading for the transcript, where Steph and Padman discuss:

• Why Dr. Padman Vamadevan explored entrepreneurship as a better way to solve cancer than staying in the medical field;

• The learning & unlearning as part of the founder process - and how the Oneday MBA program helped him;

• Bringing in an external CEO to lead the business Padman founded;

• Fundraising lessons;

• And more...

PV: What I realized in being a doctor that we are incredibly good on the front line of solving problems that are non-complex.

We're brilliant if someone's got a pneumonia, if someone's got a heart attack, if someone's got an urgent problem where there is a simple guideline and there is a formula that works, there is nothing better than the health care globally, I think, for that. I think where I got to, and I'm my background is that I am a traditional, doctor that sort of works at the nexus of oncology and something called clinical pharmacology, which is really understanding what drugs really do in the body - beyond just what they're licensed to do, can they do other things? And so working in a healthcare setting for about 16-17 years, treating some patients successfully and not doing so well on other patients, what I really realised, the current healthcare system isn't geared towards dealing with complexity.

And that's not an intelligence problem. We've got some of the best minds, the best scientific minds in the world. We've got some of the best doctors, people that really care. The problem is really that they're not getting the right data. They're not getting the connectedness between their intelligence globally. Everyone's working in silos. And that doesn't enable us to deal with personalized care. Or deliver personalized care, I should say. We end up doing is sticking to guidelines that are kind of for the many, not the few. And some of those guidelines will work and some of them won't work so much.

And so that was really the problem that we were trying to solve at Astron, which is to say, look, in cancer, this is an incredibly complex disease, and there is a massive technological overhang. We can now tell you in incredible detail what is likely to have caused your cancer, how your cancer is behaving, what pathways is it using, what might happen next. But the problem is that the tools that we address that complexity are really, really limited. You know, drug development takes years and costs billions, and we we deal with cancer at a sort of node level. We find a gene or a biomarker, and we try to deal with that one at a time. And that's not really how cancer works. You know, it evolves, the rest of the network doesn't really get hit, and the disease adapts really quickly.

And so that's why at the moment, I don't think we treat it as well as we possibly could. And that's really why we built Astron to try to fully understand what the triggers, what the drivers, what's enabling your particular disease to grow, to thrive, to behave in the way it is, what's gonna enable it to thrive in the next five years, and try and get ahead of it. By being just smart.

SM: Incredible. Such a noble mission, Padman. What was the trigger for you to explore solving this problem through entrepreneurship? And, like, what why did this motivate you so much to explore another path in solving this?

PV: I think because I realized, looking you know, I I was on a path to becoming an academic clinician, probably within the NHS and with a university as well. And I realized when looking at, you know, some eminent professors who are above really wonderful people actually, and just having really honest conversations with them and and talking to some at the end of their careers about what they felt they'd achieved. And when I talked to them, what they achieved was publications and, you know, education, educating the next generation of doctors.

And when I actually got really deep into it and said, look, how many patients did you actually, you know, did your research translate into benefit for? Often, the answer was very few. And I'm gonna be honest, slightly controversially, I hope they're not listening here. But the main trigger for for this, I think, was I I was very happy working as a clinician. I loved it.

I love seeing patients. It's one of the things I really miss now. But, actually, I went for dinner not too far from here with two of my very good friends from university from my time at Cambridge and both economists. One works, very high up in the kind of, food marketing, sector, and one is a CEO of a an IPO company in The UK. And I spent an hour listening to them talking about everything that they'd achieved.

And after that, they looked at me, and they were asking me about what I was doing and, you know, the change I was making, and we had published a paper recently showing that we had sort of doubled survival in, a particular type of brain cancer. And that paper was about ninety six patients. And I expected them to to sort of be really wowed and dumbstruck. Yeah. And the question they looked at me and said, well, has that changed any guidelines?

And I said, well, no. We we would need to. That's gonna take billions and decades and it might not ever be possible. They said, okay. And how many patients do you see every week?

I was like, you know, thirty, forty, 50, whatever it was. And they said, well, look, Pat, that's great. But how is that scalable? Like, you're one person. You're a smart person.

There are lots of smart people. How do you scale that into population level change? Because clearly, you've got something, but how do we move the needle for people around the world? And I was really annoyed. I was sitting there thinking, come on.

Look. I'm trying to deal with cancer beer, and you guys are like, you know, you you build yeah. You're building food umpires and selling cards and whatever else it is. Absolutely brilliant guys. And I didn't speak to either of them for a few months afterwards.

But it was the shift I needed in in a mindset. And I I actually went back and spoke to my wife about it who's a physio, in the NHS as well. And and she thought it was hilarious. And she was like, I think they're smarter than you give them credit for here, Pat. And what ended up happening was that was one of my triggers to join one day, which obviously, you know, and it was the the movement to say, okay.

Well, look, I don't feel that I can achieve or we can achieve the change, the systemic change in the system that we need to in terms of building, you know, a framework that's gonna help people at a population level scale. The NHS isn't built to do that. It's it's built to help one patient at a time do an amazing job. It's got fantastic people in it. But to build systemic change, I firmly believe that you need companies that are geared just to doing that, to to being completely focused, radically focused on just that aim.

Mhmm. To getting the right data, connecting the right team together, connecting those silos together, working with the NHS, working with all those people, but with one singular goal. You'll be playing a completely different game, haven't you? Like, one of the foundational ingredients for any type of success, which I talk about, is first and foremost, is play games you can win, select the game that you wanna be playing first. Yeah.

And so you recognize the advantage that you've had is, like, being on the inside. You've got that not firsthand experience. You've observed everything that's happening. You've then you've come to that realization that, okay. We're not gonna solve this problem in this way or in this timeline through this existing systemic structure, and there's a reason for that.

We get that so we're not bashing how that works. Really interesting about that dinner, and I love hearing about those specific trigger moments. It's really funny how that bruised your ego as well. I'd be I'd be the same. I need a few months to, like, lick the wounds and then come back again.

But that was exactly the sort of thing that was crossing my mind as I was listening to the early story. Right? Like, I was wondering, like, were your parents entrepreneurs? What was that sort of exposure that you had? Because you mentioned that, okay, maybe they're smarter than you.

I doubt that. But it's more the fact that these two friends of yours, they just come from a completely different world. Right? They're in the private sector. They're both economists, as you said.

It's much more commercial, capital it's like that completely different viewpoint, and that's where it's really healthy. When we talk about diversity, it's not just the color of our skin or our genitalia. It's that diversity of thinking as well. Absolutely. So your next step so you kinda came to this region.

I can imagine in terms of, like, your self identity, you've had many, many years as a medical practitioner. It must have been such a shift for you to move into being an entrepreneur. Yeah. I think even, like, now, would you call yourself an entrepreneur? No.

I actually call myself still a medic, but I'm just practicing medicine in a slightly different way is the way I think about it. And it's interesting. My my so my background is that both my parents are doctors. My mum, is a psychiatrist. My father, an ENT surgeon back in Sri Lanka, but when he moved here became a a GP doctor.

And when I said that my wife said that they were smarter, what what she actually meant, they are smarter. But what she what she actually meant was that they had an ulterior motive. And, actually, it's it's quite funny because after Astron was set up, and, you know, we started doing what we're doing, I went and spoke to one, and he and he said to me, he had a twinkle in his eye. He said, Think we're ever It worked. No.

Well, not not quite that, but it did work then, didn't it, Pat? Because it's taken you five years to kind of take the hint that you could be building something here. We just thought now is the time to be harsh. Give you that because first. Yeah.

So so it was actually they'd they'd actually curated a plan, I think, beforehand without really between the lines. So I kind of owe, their what can I say? Robust fronting up to a to a really good mate. But it worked. So Fantastic.

Good day. So those first steps so first of all, you've got that kind of, like, okay. Having these outside perspectives and opening your mind to these other ways of solving this problem Yeah. What are those first steps that you take on that part entrepreneurship? You mentioned one day so as context to our listeners, one day is, an EdTech scale up.

It is, an MBA program built by founders, for founders. I'm a mentor on the program, very fun. And the best thing about it is that fully academically certified, you get an MBA at the end of it, but the best thing is is that you start and scale a business for real alongside it. So that's how Padman and I are connected. He went through the program.

I was very, very impressed with his final capstone assessment. So that's just a bit of context for the listeners. Was that the first step for you into entrepreneurship? And if so, why did you choose one day? Yeah.

It's, again, a great question. I think it took a lot. I think when you're comfortable in a profession that you love and you can rationalize everything that you do every day Mhmm. By the fact that I I took an immense amount of just pride in helping patients. You know, my pride, they all have my mobile number.

I I, you know, spent as much time, you know, in the evenings on the phone to people, you know, reassuring them, talking them through things as I did in clinic doing. So, it's just kind of one of the professional, not duties is the wrong word, but, responsibilities that I I took on. I as I said, I really miss that. But I think for me, I've always been someone who has had lots of, across loads of different things, actually. And the difficulty was always knowing how to translate those.

And I'm always someone that tries to build the perfect product before standing up or letting anyone else see it. And, actually, one of the first things that I that I learned the reason I chose one day really was it was about building a product Yeah. Not just learning the theory because I thought I'm great at exams. I can, you know, I can learn the theory and I can pass that, and I have another, you know, MBA, and then I'll go on for another ten years exactly what I'm doing now. I like to do, like, certificate.

Exactly. I was like, this is the safe thing to do Yeah. But it's not the right thing to do. And it was actually my my dad that said to me, look, you know, take the risks that I never did. You know, he was a GP and loved it.

That's so great. But but I think he looks back on his career now and thinks he wished he'd done other things, with his time as well as being a doctor. And that's what attracted me to Wonder. I think I think it wasn't really about the MBA, the certification, although that's obviously nice. It was really about the ex to people who had been there, done it, you know, really simple things.

I was fantastic at being able to tell my stories. You can probably hear in a thousand words, in a really complex way where everyone would fall asleep by the time I got halfway. And one of the first things I got from one day was that you have to simplify things. And if you can't tell your story to everyone and they aren't interested within the first three sentences, you've lost them. And that was it.

The other thing that was a really early learning, actually, I think one of the things that I have really taken to heart and it's changed me most in just my personal life and my interactions with people is all about that perfection goal. You know? And I I think it was, Ben who's now my CEO, who was my mentor at one day, who came up with the whole I didn't come up with it, but he he told us about the whole boy's law of iteration where, speed of iteration beats quality of iteration every single time. And I don't know if you know the the kind of story behind that. Sure.

I'm sure oh, okay. Okay. Story actually, it stuck in my mind where, there was essentially, two particular type of fighter jets, so MiG sixteens and f 80 sixes, I think they were. And essentially, the MiGs were better than the f 80 sixes in every single way. They were more maneuverable.

They were faster. They climbed faster and higher more quickly. They were built better, etcetera. And the f 86 were older. They had two advantages and that was it.

They had, I think, sideway had slightly better visibility from the side, and they had hydraulic, you know, controls. And that was it. And every single person in this field predicted that in a dogfight, the MiGs would win every single time. And actually, what happened was that the f 86 has won, I think, nine out of 10 times. And it was super interesting because Boyd went back and looked at it.

And he said, well, look, The MiGs are higher in quality in almost every single metric, but the f 86 win. So and it turned out that he thought that actually maybe it's the speed of iteration. And the speed of iteration being that that hydraulic control that the f 86 has had meant that the pilots, even though, you know, the MiGs couldn't get sorry. The f 80 sixes couldn't climb as fast or weren't as easy to turn or whatever, the actual control to turn them was a lot easier for the pilots to use. Mhmm.

It required less muscle Mhmm. And less time, and so they were able to just move much more easily. Mhmm. And so over time, in a dogfight, when you have to do that same movement loads of times, you could iterate really, really quickly because the lever was easier to turn Uh-huh. The manual one.

And that's why they won the dogfight every single time, kind of where that learning comes from. So from my point of view, in medicine, you get rewarded for staying within guidelines and not making mistakes. Yeah. Exactly. And it's really half.

The opposite. Yeah. Exactly. Yeah. In in entrepreneurship, you kind of you get rewarded for making mistakes quickly enough so that you still got some runway left in.

Completely different world, isn't it? And, again, with, like, safety and, you know, putting patient first. Yeah. Like, it's it's looking the opposite, like, you know, rightly and very understandably so. Right?

Like, you can't take risks, move fast, and break things, all of these subtourisms. So you shared the Boid's law as a story that really stuck with you that helped to kind of better sort of, I guess, appreciate the importance of the speed of iteration. Was there anything else for you that helped that sort of mindset shift? Because you've been living, eating, breathing this whole other world for many, many years. And so I'm I understand that, like, it's not as easy as but, okay.

I'm gonna be an entrepreneur. I'm gonna, like, just learn how to work in this completely different way. So was there anything else that I guess helped calm that sense of perfectionism, doing dotting the i's, cross crossing the t's? I think it was that there's so many cases in medicine where I thought I'd done everything right. Mhmm.

And I didn't achieve the outcome that I wanted for the patient that was sat in front of me. Mhmm. And I was sitting there thinking, I've done everything. I've done every test. I've read every book and paper.

There was nothing within the confines of the system that I could have done differently, and yet this person in front of me interesting. Hasn't had, you know, the the results that I would have thought. And yet, there's someone else over there Yeah. Who's done all the things that maybe they shouldn't have done, and they've done really well. And they've succeeded.

And I can't explain it. And and I think that's why I think the problem that we're trying to solve really I mean, fundamentally, and this might be a bit controversial. I see this all over my timeline that AI is going to cure cancer. And the thing is, from my side, it might well help. And and I hate using that word cure, but it might help turn cancer into a chronic disease, which is what we're trying to do really into something that is manageable long.

But the main problem is that it's a data problem, not an intelligence one. We aren't collecting the right data. You know, we're not collecting enough data. We're not collecting broad enough data. We're not collecting it frequently enough, and we're not sharing it with the world.

Mhmm. And I'm not you know, our team is wonderful, but we haven't got all the answers. We haven't got all of the expertise. And so one of the things that we are really focused on is we got a use case scenario for collecting broad, deep molecular data across cancer types. But we wanna make that so that other people who are asking slightly different questions can interrogate it and use it and move this forward.

Yeah. And it's a really it's a public health, you know, decision there. Yeah. You mentioned the buzziest word of 2025, obviously, AI. How much of AI are you incorporating in in to Astron?

A fair bit. And I think where we are with ASTRON at the moment so, essentially, what we are is a bioinformatics platform. So the way I think about, cancer management at the moment is that we we talk about precision or personalized medicine. But the main problem is I make technological ovaing, so we we know that there are over 750 different cancer driver genes, okay, at the at the sort of DNA level. And that's one level at which you can look at cancer.

There are many other levels. You can look at how those genes are expressed or what proteins they're creating or what metabolism of the person is or, I don't know, the immune system, etcetera. So just looking at DNA is one bit. But there are 750 potential cancer driver genes. And at the moment, precision oncology can only hit about 50 of them.

Okay? And in any one cancer type, we're kind of two or three drugs hitting two or three genes, and that's it. So precision oncology is for the few, not the many at the moment. And the problem that we are trying to solve is that drug development is absolutely vital. Don't get me wrong.

It's the cornerstone of the next frontiers in oncology. But drug development is costly. You know, we're talking 2 to $4,000,000,000 to create a new drug or vaccine, and, and it and it's also really time consuming. It takes ten to twelve to fifteen years to develop a new drug lot of risk for a drug company. You know, you're you're finding now because the costs are so high, the cost of developing a new drug double every nine years or so.

It's ridiculous. Only drug companies can afford to do it, really. Universities can't. Charities can't. It's really hard.

Can I just say on this point on cost, what you make, come up in my mind for me is Yeah? The extortionate, unbelievable, eye watering amount of investment that is going into AI to bring that back up again Yeah. Into, like, the technological development, the the servers, the all of everything in this whole kind of AI industry is so, like, incomprehensible, the scale of it. Yeah. How does that make you because it's like you can like, anything in like, you can find the saloon.

You can you can find the money. You know? If it's if there's, like, enough of, like, a capitalist need, enough of, like, a political you know? Look. I think it's I think, fundamentally, we're in a system where we're trying to create one drug for one alteration, and that's a problem.

Yeah. Because you're dealing with it of a really complex disease. Yeah. You deal with one node, and and if you're lucky, it works for a while, and then the disease evolves anyway. Yeah.

But the problem is if you think if you're thinking about a drug company and they've identified that there is already a drug out there there for a particular molecular alteration. And that drug works really well. And it's worth, I don't know, $50,000 per month to the drug company per patient. Okay. That's an incredibly, you know, rewarding target to hit.

And they think, well, okay. There's a drug out there that that works, and that company is doing really well. How about I hit the same target, but I use a drug that doesn't have to be injected? It's a tablet. Mhmm.

Or it's got better side effect profile or whatever it might be. Just like optimizations and improvements on something that already works. That's hugely derisking it. So Yeah. Yeah.

When we're talking about hitting more targets, that's not how drug companies are incentivized. They're incentivized to hit targets that they know are gonna work. Yeah. And so our approach, Astron, is we can't solve that problem with new drug development. That's absolutely vital.

It will continue a pace. And and the AI bit that you mentioned, actually, in drug development, the hard bit is even if you expedite the bit at the start where AI is really useful, identifying better biomarkers, etcetera, that's just the kind of first 20% of the process. You still have to go through phase one, two, three, and that takes time because you're looking at outcomes and safety and all that kind of thing. So we're still talking about an eight, ten year process Yeah. Ultimately.

So from our point of view, where we use AI is we say, okay. Well, look. There are hundreds of thousands of papers out there that talk about drugs, oncology drugs, non oncology drugs, supplements, lifestyle interventions, anything that can have an effect in your body. And there are papers in the peer reviewed literature that talk about how those interventions affect different pathways, proteins, DNA. And as Astron really is we've created an engine which is, LLM, I suppose, which is incredibly, effective and specific at drawing out interactions between an intervention and a target.

We then score that intervention using our expertise, on multiple different criteria. So how good is the literature? What kind of studies were there? Who published it? How many patients were there?

Were they just in animals? Were they in cell lines, etcetera? How good is the actual binding data? So how well does that drug actually bind that target in real life? How safe is it?

Is it relevant to your particular cancer? Amazing. How important is the gene? And all of that is really important because patients often ask me, how is this different from, like, Gemini or GPT? And the thing is, we're not just trying to find any pattern because you can literally go to GPT or Gemini and put in any drug or any supplement and any disease, and it will find you a way that they're connected, and it might be, you know, useful.

That's not science. That's not useful at all. That's just finding trends Yeah. That be completely wrong. Yeah.

And the thing with patients, and, you know, this is the hard bit, is that they explore this journey once. They do it once. They it's really difficult for them to be experts because you're kind of constantly learning from what's going wrong or right, but they have one chance to get it right. Yeah. Is there a more personal driver for you with this?

The surprising answer is actually not, which is I think for me, I like challenges, and cancer has a really special place from a psychological viewpoint. You think of someone with a diagnosis of cancer, and you compare them with someone with a diagnosis of, I don't know, heart failure or, dementia or diabetes even, and people think the worst about cancer. And the reality is that there are lots of cancers that people do really well from and live long, healthy lives. But I think for me, I actually trained initially in cardiovascular medicine, and we became really good at treating heart attacks and strokes. And that was kind of like the reason that I thought, okay.

Well, they've achieved a lot there. What they did with the disease kind of mirrors what we're trying to do in cancer, which is if someone has a heart attack, the acute problem is you've got a blockage in a blood vessel, and you need to put a stent in or a bypass and restore the blood flow, and that's great. And that really helped. But, actually, most of the research dollars that were spent in cardiovascular medicine was on the secondary prevention. Like, how do you prevent that person from having another heart attack or stroke?

Well, you identify the risk factors. Have they got high blood pressure, high cholesterol, diabetes? Do they smoke? Are they taking cocaine at weekends? They're not doing enough exercise, whatever it might be, and you really aggressive in that.

And you measure, you know, what you're doing and what impact that's making. And failure for a cardiologist is that person ever having a heart attack or a stroke again. Okay? And in cancer, what we do is typically most of the tools and there are some newer tools which do different things, you know, in a in the case of, say, immunotherapy. But typically or historically, most of the tools in cancer, surgery, radiotherapy, and chemotherapy are designed to kill cancer cells.

Okay? So eliminate as many cancer cells as you like and then hope it doesn't come back. Right? We're not really hitting those prevention factors that affect how likely it is to come back. And that's where I thought there was a real job to be done.

Mhmm. And, obviously, cancer is much more heterogeneous, much more complex. We're not talking about five or six risk factors. We're talking about, you know, 750 to the power of three or four. Right?

So that the combinations are infinite. Wow. Which is why the toolkit has to be massive. We're not just talking about five or six different drug types. We're talking about potentially thousands.

Wow. And that's why conventional drug development can't solve this problem. Mhmm. And so the first bit is collect data on the right patients at the right time. The second bit is being really practical with how we hit this.

But for me, I think I just established that I wanted to big challenges are what I feel like I'm here for, if I'm completely honest. I'm very happy to hear that. I just yeah. I just I I and I just I I thought that cancer was although I wasn't directly affected by it, actually, for me, that means that I can slightly depersonalize it. And don't get me wrong.

This is exactly what I was about to say. Yeah. Yeah. Sorry. Carry on.

No. Please. No. I was just thinking exactly that, you know, with your opening answer on, like, it might be surprising. Like, actually, it's not surprising to me.

And I was just thinking that. I was like, it enables you to be perhaps more level headed, like, emotionally affected Yeah. And just eyes on the prize, like, focused on the problem you're solving, leveraging your experience, your expertise, the the the kind of technological age that we're in as well, and how you can apply AI and data to to just solving the problem. So almost more effective in that sense. Yeah.

I I think it makes it it certainly helps with that. It also takes the ego out of the game. Yeah. When you haven't got, like and and don't get me wrong. Every patient is a is a horse in the race for me, and I I want to do everything Yeah.

Cool. The horse. Yeah. Absolutely. As long as it's safe and etcetera.

But it does actually mean that I don't have a particular philosophy that I wanted to do. Exactly. We're trying to kind of find the truth for every patient Exactly. Get the best information Yeah. To deliver the, you know, the interventions in the right way for that person's version of the truth.

Yeah. Yeah. And that I think is actually a bit of a superpower. Exactly. And it also kind of leads into the decisions we've made around, you know, who's in the team and and and how we're growing as a company.

Amazing. Well, you've handed on a perfect segue because the other thing I wanted to ask you about is you mentioned that your mentor on the one day program, which we touched on, is now your CEO. Yeah. So I'd love to hear about that. Like, what was the decision making process with that?

How did it feel bringing in an external person to come in as a CEO to run the business that you founded? Do you know what? It was the easiest hard decision I think I've ever made. Really, really, really, really easy. Ben is like yourself, Steph, just an incredibly humble chap.

He is I am a humble chap. Sorry. My apologies. I love it. I love it.

I've got the masculine energy. No. Not at all. He is yeah. He is he is obviously, as you know, an incredibly successful entrepreneur.

He has the scars, the things that have gone really right, the things that have gone really wrong. And if I'm honest, I think one of the things that one day taught me was to really double down on the things that I'm good at already. Amazing. And and to be quite frank, I haven't got the time, me personally, but patience and science hasn't got the time for me to learn how to be a good CEO. Love that.

You know? I'm, you know, a middle aged man. I've been a doctor for a long time. I've been a scientist for a long time. My expertise lies there.

That's what I think I can deliver for patients. That's what I want to deliver. Do you know what I'm just observing or so sorry about the, the lovely compliment that I will take about being a humble chap and My apologies to everyone. I love it. I love it.

But that what's really funny is, like, that humility that you recognize in Ben in that you just have in buckets, Padman. And this comes out here as well even with this decision to bring in a CEO. Like, we mentioned before about not having any ego in the game whatsoever. Like, there is the ego has, like, left the building with you. Right?

Everyone could do with having greater self awareness, but especially founders, especially entrepreneurs where I I get how difficult it is when, like, it feels like it's your baby, it's your mission, you're so dedicated to it, you've got that conviction, you've got the vision. I totally get but sometimes when you're able to put some of that ego aside and really, truthfully, objectively ask yourself. I think with you here again with the huge problem that you're solving, I think that's I think you're already incredibly humble and knowledgeable and lovely and super high self awareness anyway. But when you combine that with this life or death problem that you guys are solving, it just means that...

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About Stephanie Melodia:

As ex-CEO of an award-winning marketing agency, Stephanie now hosts Strategy & Tragedy, advises MBA students with Oneday, coaches founders 1:1, and travels the world as an international keynote speaker on her signature subject of ‘Hacking Luck.’

She has delivered impactful sessions for household brand names including Qatar Airways, Soho House, WeTransfer, LinkedIn, Xero, Harvey Nash Group, Web Summit, and more - leaving audiences feeling inspired, motivated, and energised.

Book Steph to speak at your next event here.