If we look at the future of our carrier, the World Economic Forum predicts that within five years, we will need all to update or change our core skills.They also predict that within our lifetime, we will change three to five carriers, and they don't mean roles. They really do mean carriers. AI will create 78,000,000 new jobs, but the problem is we don't know what kind of jobs. Right? So we can't really predict, like, the roles and everything. And so what they also see is that the generalist will be the new specialist and the, you know, the diversity of our experience will matter a lot. However, the problem is that the job market today is still evaluates people through the linear carriers. Right? And they're not adapted to our nonlinear world where that's normal to go across countries, you know, and speak several languages. So let's talk a bit about me.

So as I mentioned, I'm Oksana. I was born in Vladivostok, Russia where is on the Asian side. So with the Japan, North Korea, and China is. So really far away. That's the biggest force of pressure. So we then moved to Moscow, and, I I will say I grew up in Germany, in Berlin, because that's I got my education and became an adult. And now I'm in Norway in Oslo. And as I mentioned that today is totally normal. Right? Move across the bold borders, speaking, several languages, and, navigate between culture. And, actually, I think that diverse geographical background is our built in superpower because that gives the ability to read the room and don't have a filter of, like, a local opinions in the sense of, like, in every country, there is, like, usual, like, general opinions, views, political, cultural view.

But for people with migration background, it's actually really nice because we don't have all this filter. And so we can read rooms much better and have maybe a bit of less day bias, but our own bias. So what the papers paper said about me and what was I'm actually doing? So the on the paper, I have education in linguistics and it's master of arts. Right? So an international background, and I do not have technical degree. So if HR sees this, that's what they see. However, that's what I was actually doing. I was doing connotation of and creation of datasets. We did a lot of experimental design where we were looking at language and speech from physiological perspective. That means e g eye tracking, acoustics, physiological data. The video you see, it's, from my, PhD. Right? So, it's it was actually totally something different than on the paper.

And I've done a lot of signal processing in MATLAB, for example, as well as statistical analysis. On my personality side, I'm actually visionary. I'm a hustler. I'm a connector, and I'm a builder. And that's why I was all the time. I even won once this hustler for life, what you see on the picture. So that was my personality type. So what does it mean? It means that none of this fit a job description or pass through the HR test because on a paper, I had master in linguistics. Right? And the jobs which was interesting to me, they couldn't understand my background. So I moved from research in linguistics and speech science to my manager at Technical University Berlin, where I was responsible of building robotic labs for research of intelligence.

And they looked at intelligence from perspective of biological perspective in a sense of swarm fish, right, and humans and also computer science. And that where my background and all of this data diversity was really matter. However, they had problem and difficulties to hire me because I had master of linguistics, and they were computer science department. So they had to fight to hire me, and that was my first manager job. And from that, I moved to the industry. In the between, I was doing also network, a woman in big data in Berlin, helping women. And then I moved as a project lead into the industrial automation where I was leading projects on implementing AI into factories and drama railway Siemens Energy.

And the interesting part was that in that jobs, I haven't worked with factories before, but they did had a lot of r and d projects with this company and research institutes with grants and application, and that's what I was good at. Right? I could write reports, grants. I knew exactly how to publish paper. And on the same at the same time, I was a manager leading the projects. So that was actually a really great fit, and that's how I've got to learn a lot about factories and other type of industrial automation. And today, I'm a founder and building on intersection of all of it, but I will come to it a bit later. So from the listings to AI in factories, right, I had experience with a lot of different data types from my linguistic studies. I had a lot of domains attached. And but every way it was underlying, like, structure. Right? And so what it gave me, I could do pattern recognition, fast onboarding, cross domain translation, and systems thinking.

That was something which gives me ability to move fast between domains and to basically be able to translate this knowledge. For example, in product and business, in AI automation, and industrial systems. But if we look at these four disciplines, let's say, what we have, we have on the one hand different worlds, but the structure is the same. What I realized at some point that the vocabulary was always the same. For example, in research, in my academic career, we did like hypothesis, experiments, subject interviews. But this is the same playbook in a product development. In a product development, you do a product discovery. Right? You have a hypothesis, then you go, you iterate the product, you do user interviews, discovery, and then you analyze the data. What does it actually mean?

So you validate or invalidate hypothesis. In the business development, you do the same. You do market validation, business experiments, customer discovery, and well, entrepreneurship combines product and business. Right? But you have a vision, and you have hypothesis about the future and about your product. You have to do market validation, customer feedback, and discovery. So and how you do it, it's even better if you, for example, have academic career because you know how do this systems thinking, you know, try to avoid bias and actually look what the data exactly says. So what I realized at some point that the worlds weren't so different, it was just about the translation. So what could I do with my career? With my one background, I actually could become a data scientist, stay in research. I could be a product manager or a business developer. I could go to be innovation manager or AI consultant. Right? So it was the same experience, but actually all of that paths were open to me.

And believe me, at that point, I didn't actually see that. I was really, really sad thinking about, oh my god. What I shall do with my master of linguistics on the paper. So this what I show you, it just came later. Right? So all of these paths were open for me. But the problem was was my personality. Well, not the problem. Right? But the personality actually determined where I was going. So because my superpower was a chameleon, what does it mean? So it means I can move across domain cultures in hierarchy. I can adapt the knowledge across domain and translate them. I can navigate cultural codes, so I can work with different markets. Right now, for example, my startup works with UK with US, you know, with Arabic Emirates. Right? So that's much easier for me.

And, also I can move between workers on the factory floor and c suits and give them feeling that I belong to them. So the skills I have, they create the possibilities, but the personality was the one who determined what I'm actually going to do. Because for my personality type, for example, I couldn't be data scientist. I don't have patience or attention to details in that case. So now I'm building Amex. This is my startup company. Right? It's a deep tech company based in Oslo, and I'm building autonomous troubleshooting for unmanned and remotely operated systems. It sounds fancy. However, it's actually compounding of everything I've done before. When I worked in industrial automation, I've done a lot of project on anomaly detection, and I was working with robots. But my first job in as a manager at technical university, I was doing at science of intelligent projects where they did biological intelligence study against computational intelligence. Right?

So and I think that inspired me a lot to say that I'm building immune system for machine. So I took a really well known problem of machine health and put a different angle on it. And I said, we need to understand machine as a whole with all of the interdependency of components, and we have to understand the root cause. So it actually had to act as an email system. And what I found interesting is that everyone was like, oh, yeah. That's a really interesting angle to this problem because the problem is not new. And so I think that the ability and my experience, I just told you and described you, gave me a possibility to think about a problem from totally different perspective, started to approach customers and investors with this, and everyone found this really interesting.

And everyone says, like, oh, we actually like the way how you look at it. And that's the feedback I had when I started to talk to customers. So eventually, as a startup, combined everything. Right? So my personality, so I can do partnerships, collaboration, and, you know, innovation storytelling, but also my skills. Because this idea requires a lot of data science. It requires cross domain thinking. Right? And also understanding from how the industrial world works and robotics world. So all of that together came together. And this is how you think, like, okay. How how do you come from, like, linguistics to AI? However, I have to say also that when I was studying linguistics, always had a passion for new technology and innovation, And I always wanted to to kind of belong to to this space. Right? So I was kind of looking how I can translate.

And so I do think that the driven force behind it is not only your skills, it's your interest and your personality. Right? And then you can combine skills and translate all of this. And so now, for example, when I come to maritime industry to sell my product, right, I say, I grew up in Vladivostok. I was born in the biggest port of Russia, and my father was a seaman. So in that point, they connect to me. They're like, oh, wow. That's so cool. Right? They start to ask questions. So we have the leverage. We start to talk, and they feel like, oh, I belong to this industry. Right? When I come to, like, industrial world, I say, I worked in industrial automation in Germany, and people like, Germans engineering. Right? Industrial.

That's what they think of, you know, Germany. And they're like, oh, that's, like, cool. Right? So I have a credibility there as well. Recently, I applied for European Space Agency grant, and I also got a soft funding public grant from Innovation Norway, which, like, €100,000, which only happened because I have ability to write public research and grants from my previous academic career. Right? And so that's what I mean. Like, I use every single stop in my career and my personal background to use it in a certain situations, which gives me a lot of leverage and credibility in that moment. And so I would like to also think about the error which is coming as AI. Right? And and in AI, we'll automate our routine specialization. Right? It will create, like, a lot of task, repeated task, will remove it. But what our role will be in it?

And I think the innovation will come from people who stand between discipline cultures, who can build connections with you know, which makes sense. And I think that we don't do it enough. We only concentrate on, like, five years data science experience, five years this, with who you are as a person, where do you come from. Because all of that is a superpower to be able move through countries and, you know, talk to people with different cultural background in different languages. It gives you a lot of leverage even if it's not might be impressive on the paper. And so what is actually the perfect recipe? Right? And the perfect recipe, I think it's to know your direction. So in a sense of, like, you know where you wanna go.

So you wanna be a space, you wanna work in the space industry, but you're not space engineer. Well, I don't think it's a problem. You still can do that. You know, you just have to lead with your curiosity and passion, and then you have to combine ingredients. Your personality, who are you? Are you the person who pay attention to details, you know, who can be really good data scientists, or you're a hustler type? Who are you? And then you put your experience in it, your cultural background, and your skills together everything in one, you know, place. And then you learn the vocabulary of the new domain, and you show up as a bridge. Right? You say, I've done this, but now now I want to do this, and I think that's transferable.

And for example, now I'm working with German Aerospace Institute on a project of machine learning for satellites. Right? But satellites is just a different machine. But the machine learning, you know, it's stays the same across disciplines. So I can advise to follow your interest, combine your story, and translate your skills in it, which actually will open you open up. And now when I come some people say that, oh, you're like this typical engineer. You think so too complicated. I'm like, I'm not engineer. And but I take it as a compliment because I think, well, actually, they perceive me right as one of them. And I think we don't do it enough. I think we look only on our skills and on the professional carrier.

But as I mentioned before, I do think that our background, cultural background, our personality matters a lot. If you put all of this together, you can actually open any domain in any door. So I hope you, yeah, learned something from me today or got inspired. Just please connect with me on LinkedIn. Write me messages. And, yeah, basically, come to me anytime. We can talk about it or share experience. I always think it's really interesting to hear from others as well. So please do, and I'm happy to share anything else. I think we have one minute. Yeah. Yeah. Really cool comments. Please add me on LinkedIn. Let's write. Let's chat. And, yeah, let's get to know each other. I think that's, like, super cool because I really wanna hear other stories.

And even so, I talked today about, like, all of those things, and maybe it looks like, oh, you know, she've got it. But most of the time, I was actually really desperate and didn't know what to do. So it's now looking back, I actually know how it all combines together. So please share your stories, and thank you so much for being here.