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SheCanCode's Spilling The T
SheCanCode's Spilling The T
What I wish I knew: Advice for women in tech
In this episode, we chat with Galina Chernikova, a data scientist with a background in applied math and a passion for AI, machine learning, and empowering women in tech.
From her work with Girls in Data to inspiring the next generation of STEM leaders, Gala shares her journey and her mission to break down barriers through mentorship and hands-on machine learning workshops. Tune in for a quick dose of insight, inspiration, and ideas for a more inclusive tech future.
SheCanCode is a collaborative community of women in tech working together to tackle the tech gender gap.
Join our community to find a supportive network, opportunities, guidance and jobs, so you can excel in your tech career.
Hello everyone, thank you for tuning in again. I am Katie Batesman, the Managing Director, Community and Partnerships at she Can Code, and today we are discussing what I wish I knew advice for women in tech. I've got the fabulous Galina Chernikova, also known as Gala, with us today. She is a data scientist and she has a background in applied maths, with a passion for AI, machine learning and empowering women in tech. She's here to share a little bit about her journey and her mission to break down barriers through mentorship and hands-on machine learning workshops. Welcome, carla, it is a pleasure to have you here. Thank you so much for joining us.
Speaker 2:Thank you so much for hosting me. I think you just said that we decided to name this episode what I wish I knew. But then I was reflecting and I saw that I want to take a slightly different angle. I actually would be highlighting what I didn't know. I love that and how it ended up being a good thing. I love that.
Speaker 1:I love that. Yeah, that's a really nice angle to take all the things that I didn't know and now would like to share with everybody else. I love that. Can we start with a little bit about background about you? Let's set the scene with a little bit about you, where you're from, how you got into tech.
Speaker 2:that would be great, please away from how you got into tech.
Speaker 2:That would be great place, yeah, sure so, um, I was born in kiev, in ukraine, uh, but I went to school and into university, uh, in moscow, uh, in russia.
Speaker 2:So I think that's quite an important highlight, because the way how people approach tech industry in Eastern Europe is a bit different from the UK and from Northern Europe, so that's why I'm highlighting it. So I went to school into like a special class where I was studying mainly physics and mathematics, and I really wanted to go into physics. But then I started looking at, like, my friends who were a couple years older than me, who already start pursuing their degrees in tech field, and I noticed that all of them ended up in tech, even if they were studying radiophysics or something very hand-on, mechanics or engineering, yeah, but all people I knew they ended up in tech and they had to refresh how to code. And I thought, okay, I'm doing physics and advanced maths for my, let's say, russian type of A-levels, but I decided to go into computer science, yeah, so I really wanted to have similar job title as my degree. So that was critical for me.
Speaker 1:Yeah, I hear it's so interesting. You say a lot of people that you knew were going into tech because we don't have that problem in the UK, which is why we talk about it on this podcast. But it's great to hear that you were in an environment where a lot of people felt encouraged to to go into tech and that you already could see lots of great things in in the tech industry. Um, what actually drew you into data science from from that point and what drew you to um ai and machine learning?
Speaker 2:yeah, so that's a good question. So basically I was thinking about what profession will be in demand. So I had to make a decision on my degree when I was 16, 17. And I thought, okay, we have that vast amount of data. So now we have this fancy name like data of a profession data scientist. What is it about?
Speaker 2:Because you can be not a scientist. You shouldn't be in a lab wearing a white robe or something in a lab bearing like a white robe or something. But you still can be, even without phd. You can have this job title, data scientist. And it kind of drew my attention and I started exploring and I found it like very interesting that, okay, now we have that vast amount of data and we need to have a specific science how to work with this data, how to store it, how to analyze it, what patterns we can learn from from the data, how we can use it, and that kind of drew my attention.
Speaker 2:And the second thing that I really like about my profession that if you are a data scientist or machine learning specialist, you can work in any field. So it's not. It could be always very similar. So it will be like machine learning models. You will be coding in Python a lot, but you can be a data scientist in a pharmaceutical company, you can be a data scientist in a construction company or on BBC News, or you can do open source investigations in New York Times. So you can do open source investigations in New York Times. So you can work whenever you in any industry and you can change the industry easily and that opens you up this variety of the world, if that makes sense yeah, that's a wonderful way of looking at it.
Speaker 1:Um, the the variety of um companies that you can work at during your career once you become a data scientist. It's so interesting to hear when people go into that area because there is that misconception as well around working in data that it's going to be quite dry and quite boring and people think, oh, you know you work in data. And then actually when you talk to people like yourselves who work in the industry and say things like that how you can move around and actually the impact that you have and it's more what you can do with the data rather than just the data itself Then you kind of get more of an understanding of you know how good a career in data can be. Like you just said, you can literally go anywhere once you know what to do.
Speaker 2:Yeah, that's absolutely true. And look at, like, what is shaping the world now? What is shaping the world now? It's all about data science. All those dramatic changes in our lifestyle, it's all coming from data science and that's actually mind-blowing. All LLMs and chat, gpt appearance changed our lives a lot. All those new technologies they're all coming from data science and I just really like that feeling that I am on track with what's happening in the world, because if you understand technology, it wouldn't scare you. Because, I would be honest, I had a chat with like pioneers in the industry and all of them, even those who were like very into the field, when they firstly tried chat, gpt, they felt scared like every single one. When they tried that was like a few years ago, but I think everyone was like what's going on here? How is it like so accurate, so on spot and so fast? But when you know what's inside the box, it's not as scary as if you think that, oh, we will have robots running all over the place and they will be ruling the world.
Speaker 1:When you know what what's inside, it's not as scary yes, and the way that sped things up for so many of us as well in our day-to-day, and the things that we can now just ask at chat gbt um to help us with things really changed the way that so many of us have worked, and we even say that at our company. We look back and we go. I don't know how I used to get everything done without something like that um to help me, um. So I wanted to talk to you a little bit about, obviously, women in tech and your involvement in that um.
Speaker 2:You've been really active in supporting, uh, women in tech and the community and what sparked your involvement with girls in data and similar initiatives like that yeah, so, um, actually I was looking for a community because I relocated to London only three years ago and I really wanted to do something meaningful and find my community. And I heard about that organization, girls in Data, which is like sub organization of women in data. So they launched just I think a year ago and one of the representatives of girls in data, she was sharing the idea and values of that organization and that kind of resonated with me. She was saying that, oh, we really want girls to have those role models so they can see who are the people who work in the industry, so they can get some inspiration and they can understand what is happening in the industry. Yeah, and I found that I really liked the idea and I saw that that would be a great opportunity, first of all, for me to learn, because I think if you want to be a good lecturer or a good speaker, the hardest there are two types of audience that are hardest, and I'm doing drama school in my free time, that and I know a lot of people like who are playing on stage, and one English lady told me that two audiences are the hardest to deal with. First one is kids, teenagers, and second one is prisoners. Uh, yeah.
Speaker 2:So I thought, okay, I can start with. I know that you didn't expect me saying this, uh, yeah, but I'm just bringing some unexpected spark to this conversation. Yeah. So I started. I thought, well, I need to get out of my comfort zone and go and try to talk to teenagers and share my journey when I was 15. And I think I wasn't too rebellious because I had a good role model of my mother and I knew what I wanted to do from the age I was 11. So it was quite a smooth journey for me. But looking at my friends and looking at the children of my older friends, I can see that sometimes teenagers, they can get lost in this variety of information and of different ways, different paths and truths they can choose. Um, yeah. So I thought that I can go to schools and tell girls like more about myself, about my journey, and to make it less scary for them to go to pursue degree in stem, um or it's very brave because teenagers are very honest as well.
Speaker 1:I completely agree with. They must be one of the hardest audiences to connect with and to speak to. So, yeah, it's quite brave to think, yeah, I'll go for that age range because they do not hold back. What are some of the biggest barriers you've seen women face when entering the machine learning and AI space, and how can we break them down?
Speaker 2:Well, to be honest, I think it really depends on the country and culture. So here in the UK, I've actually seen pretty balanced representation of women and men in data and tech, which is encouraging, and I didn't expect to see this, but that hasn't been always my personal experience. I was often in minority, so we don't have this gender split in schools. I know that some schools in the uk they have both genders. Some of them are just for girls or just for boys, so it's very different. But because our schools have always been mixed, um, being in minority really felt awkward. When you're 14 and you have only 15 percent of girls and the rest are boys, and that that could, like, feel very underwhelming. Underwhelming when you're growing up, when you're just trying to figure out things, when you're changing. Your body is changing, everything is changing. Um, yeah, so it it felt, uh, very difficult to be in minority. So I think that positive discrimination really helps sometimes with this.
Speaker 2:And, to begin with, I think that everything is coming from the family. Yeah, so I read a study where parents were asked to estimate IQ score of their sons and daughters and it was surprising that they underestimated IQs of their daughters and always overestimated IQ of their sons, even parents who had one son, one daughter, and just because of this gender bias, they were always like very supportive, uh, with their sons. I don't remember the country where this this was made, but I think it was either uk or the us. I don't remember, um, I can probably check it out later. Uh, yeah, but imagine, like living in a family and in an environment when people always, like, expect you to perform worse.
Speaker 2:I think that that that changed like a lot in in your approach and uh, but I was surprised because there is no difference between genders and there have been a lot of studies that have proven it.
Speaker 2:You can just go on PubMed or on any other source and you can find that it's all about stereotypes, about environment and how people behave with different genders. So I think we have to start breaking the stigma and should start encouraging girls to go into those technical fields and say that they can do it and show role models, because when you see that many CTOs or CIOs, they're men, so you don't think, oh, that's because we didn't have, like, balanced representation in schools and we didn't have, as a coincidence, we, we, we didn't have, uh, equal representation of men and women in unis and now we don't have this representation in in technical companies. Um, so we don't think about it. We don't think about all those biases that kind of combined and led to this representation of leaders in tech. We just look at this representation and we see more men and we think, okay, women, just they cannot get there.
Speaker 1:They're just choosing not to do it. Yeah, and that that's it. Our women are just not interested in tech, and actually there are so many things along the way, um, like you said, and then, and then it gets to the point where everybody goes. Well, where are all the women in leadership in tech?
Speaker 2:what do you think? Yeah, and I think it's very important to share those stories, success stories. Yeah, yeah, because for me it was easier, because I've seen my mom. She never had, I think she never had a moment when she was thinking, oh, should I use my career or my family? She was like I want both things all at once now, and I think that that really shaped me, her approach, and when I was growing up I didn't even have that question on my mind that you have to make a choice. So when?
Speaker 2:Then, when I saw friends of my sister who is five years older than me and I had a conversation with some of her friends and they were like, oh yeah, probably I will give up my career because I want family, I want kids and I want to spend some time raising them, and then I had that thought in my mind for the first time in my life oh so the reason, an option.
Speaker 2:So we have those options. Uh, yeah, to give up your career to only to be a housewife, to raise kids, and probably that's something that you want. That's absolutely fine. But but for me, because I didn't have this perspective when I was growing up, I never thought about it, and I'm thinking about girls who had a different role model and they didn't know that there is a different option to go with their careers or to go with their careers and build family at the same time. That's why they're not trying it and we, as a society, we have to show them that there are many ways and you can let alone those biases and decide what you actually want in your life.
Speaker 1:Yeah, I couldn't agree more, especially in tech, because a lot of the companies even the ones that we work with as partners that support us, but also other companies in tech they work to make their workforces flexible for things for times like that when you do want to go on math leave, for instance, and then you come back as a parent and having that flexibility.
Speaker 1:And a lot of companies will try and have training programs in place to ensure that you can return to work and kind of do that training to get you back to where you need to be, because there are some good companies out there that are really trying to retain their women in tech. So they'll do all they can to to bring you back from that, because they understand life happens. People want to have families but, like you said, there are lots of ladies who think I don't want to have to make the choice. I just need an employer that's going to support me along the way and at least understand that I do want to do both. Um, so yeah, I couldn't agree more, uh, about not having to make that choice, and technology is a really good industry to to be able to, to not have to do that yeah, and actually I think that's a very good point, that tech is one of those fields where you can work fully remote.
Speaker 2:Yeah, and after COVID, that just changed the game and I think it changed the game a lot for women because they can choose to work part-time. Um, yeah, it's very different, uh, when you're working from home, so you're not, uh, spending too much time on commute, um, both ways, because previously it used to be just spending two more two hours on top of eight hours working in the office just to commute to the office. And now remote way of working, which you can find a lot in tech, I think, changed the game and made made this choice easier for women yeah, and we had some ladies um using about covid.
Speaker 1:We had some ladies who, in lockdown um saw what their husbands did for a living and I had quite a few ladies tell me that they saw their software developer husbands, for instance, who were working remotely.
Speaker 1:Covid didn't really affect them because they were already working from home and these ladies realized that you know what I could work from home if I retrained, I could do that and then I could spend that time with my family and I can do the school drop-offs and pick-ups and and what I need to do. And I spoke to one lady and she retrained and she went over to be a software developer at a company because her husband was doing it and she noticed it in lockdown and she's like I could do that. So you're right, it was such a game changer for so many ladies who realized that I could have that flexibility and I could work remotely and they kind of held on to that and retrained, which is great to see. It really encouraged a lot of ladies to want to take that up, which is great to see. It really encourages a lot of ladies to want to take that up. Something that you advocate for is wanting to run more machine learning workshops.
Speaker 2:What would those look like and who would they be for?
Speaker 2:Yeah, so that's a very good question. So I would love to run workshops that are approachable and very hands-on and, most importantly, human-centered. So I really want to show the root from the problem statement on how you craft the solution to solve this particular problem and the results and the results of machine learning and AI solutions for business, for charity organizations, and how they can just shape the industry. So I also think that I can actually make two types of workshops. I wanted to make like a very hands-on, technical one, series and another one for people who are just trying to start their journey in tech and, similar to what I've done in schools for girls explain who we are I mean data scientists, who data scientists are, what they do, what skills do you need to get into this tech field and what the best way to start yeah, and just to really see what your day-to-day is like, because I think that's a question that a lot of people think I kind of understand what my job would be like but don't quite understand.
Speaker 1:So it would be great to share those things of how to get in and also when you are in, what kind of projects you'd work on and what your day-to-day would actually be like, because, like we mentioned uh, we touched upon before data for a lot of people sounds like it's going to be. I'm just going through spreadsheets and that's it.
Speaker 1:I'm just looking through spreadsheets and tidying up data and that's it, but there's so much more to that, so to hear the things that you actually do in your day-to-day would be so valuable for um lots of young women yeah.
Speaker 2:So actually my goal wouldn't be to turn everyone into machine learning engineers, but to didn't know how to say it in Mystify the field. Yes, yeah, because I want people to leave thinking actually that isn't as intimidating as I thought, or I could use this in my work tomorrow. Yeah, so I think with the appearance of ChatGPT and LLM LLM it really changed the game and the barrier to enter the field is a bit lower. Or even to start like educating yourself, you can just understand what's the best way to prompt those models and you can already start doing some baby steps yeah, you are right.
Speaker 1:It's one of those industries that sounds very intimidating, full of lots of jargons, jargon and acronyms, and you kind of would look at it from the outside and think, I, I am not intelligent enough to go into that. But once you start training um and that's very and that happens a lot with the tech industry as a whole as well a lot of people on the outside think I have to have a computer science degree and I'm not intelligent enough to go into that, and then actually, when you look into the roles available, what your day-to-day would be like, it's not always the case. It does always. So just depend on um which areas you'd like to go in and what you'd like to specialise in. So yeah, demystifying the field is really important.
Speaker 1:One thing that is also really important is communities. Obviously, we are a community at she Can Code. How do you think that communities like she Can Code can better support women who are looking to pivot into technical roles like yours? There's lots of wonderful communities just like ours, but what about women that are looking to pivot into roles like yours?
Speaker 2:Yeah, that's a very good question and I think that communities like she Can Code already play such a powerful role by making women visible intact powerful wall by making women visible intact, uh, and that visibility alone can can be the first push. Someone needs to think if she she did it, maybe I can do so to take it even further. Um, I think there is a huge value in offering structured but approachable pathways. Um, yeah, like mentorship pairing. So I I love it. So I'm a mentor on chic, she can code a platform, and that, uh, that allows me meeting like a lot of new not a lot, but I mean like five, ten people who are like complete strangers to me from the very start, but because all of them they are very passionate about to move into tech or to learn something new, to learn about my experience, and that's a free option.
Speaker 2:So, to talk to someone who has this experience and on she Can Code, we have a lot of mentors who are willing to share their experience and I think that's great because we didn't have it back in the day when I was doing my research and making a decision which degree I should, I should choose, and I was reaching out to people like randomly on facebook like, oh, dude, I want to chat about your degree, like how do you find it? And a lot of people they were. They found it awkward and just didn't want to reply to me. And I understand them, because if you put like name of your faculty on Facebook, that doesn't mean that you want to talk to people about it, so that's absolutely fine. But when you have those platforms and you have mentors, people who are willing to share their experiences, shares and knowledge, that's a very good way to start and knowledge, um, that's a very good way to start.
Speaker 2:So I I think you already asked me that the question like what I would recommend from where to start. I would say find someone on uh, she can code or other communities. Find a mentor, have a like friendly chat with them to understand, like if I can start doing something as my mentor is doing, learn about their journey, try to try it on yourself and think if that's something that would suit me. Or if, if you find someone and you think, oh, that's completely not my cup of tea, find another one.
Speaker 1:Yes, yeah, have a conversation with as many people as you can to learn about different ways, different approaches, and yeah, I think that could be very helpful yes, I love that you're in our mentoring program and that you're really thriving in there, because it was started for that reason to take the awkwardness out of finding a mentor, because, you're right, you can. People will fire off messages on like LinkedIn or Facebook, as you said, and you'd be like, hey, can you help me with something? And then you'll get a lot of rejections and then a lot of people just ghost you, um, and you're starting to feel a little bit deflated. And then we would see it from the other side as well, because, uh, when we did shows and expos, people would approach us and say, hey, like I really want to be a mentor, but I don't know where to find people that want to, uh, that want to mentor. So it's almost like people were missing each other and there were so many good, willing people out there that wanted to give back to the women in tech community. We just wanted to connect the dots.
Speaker 1:So when we launched that program, we wanted some guidance in there. So it's like eight weeks of guidance, but there is a midpoint check-in. So, like you said as well, if you find at your midpoint that it's not for you, then there is a nicely worded email that you can just fire off and say you know, maybe this isn't a good connection or we didn't quite align on what we thought we were going to and neither of us are getting anything out of it, because it needs to work both ways and you can just part ways and it takes that awkwardness out, that go off and find someone else. But if you're still together after eight weeks, absolutely fabulous. Please continue your partnership, um. You know, throughout your career that'd be fab. But you're so right it was.
Speaker 1:It was we were hearing it from both sides that mentors were looking for people and mentees were looking for people as well, and I was like well, why don't we just connect the dots? Because it's so much easier when people put themselves in that directory and say, please reach out to me. You know I'm here. If I can connect with you and we've got similar skills, then then let's do that. Um. But yeah, it's, it's wonderful for us to see that all the connections made um every day and the the.
Speaker 1:We can't see the messages, but we know there are hundreds and hundreds of encrypted messages that are sent um and we just sort of think that's amazing that these ladies are making um those connections and are actually supporting each other and I think you can like um if someone is listening to us who didn't try being a mentor or a mentee, let me try to sell it a bit more.
Speaker 2:So I think it's very beneficial for both ways. So thinking about like being a mentee and having a mentor as kind of um, like session with your psychotherapist, but career-wise, yeah. So I think sometimes it could be like draining for your um, for your friends or for your um relatives to listen to someone's uh career's challenges. But if you have a mentor, it's similar to a therapist, so it's someone who is interested in helping you out, who has probably similar background. So you work, you have probably similar profession. For example, I've been mentoring one girl who is also a data scientist, but she started like only a few years ago while I had like a bit more of experience and she just needed like a view from a side on her career situation. And it's also a good thing for a self-reflection for a mentor, because sometimes I I think all of us we have this imposter uh syndrome. We don't think that, that we're good enough, uh, we don't think that, oh, I tick that box, I've done this project, and then you simply forget about it and you never. Uh, sometimes you don't get credits for accomplishing something, and when you are mentoring something and you share your experience and you're saying it out loud, you understand? Whoa, I actually do have some experience and I have something to say. And that was a good thing for me as well, because I didn't uh.
Speaker 2:When I had my first mentee, I was like, oh, but what we are going to talk about? I was like very insecure during the first session, but then I was like, okay, let's start looking at your CV or something very easy. And then I'm looking at someone's CV and I'm saying like, oh, I see like room for improvement here and there. And I said, well, I need to find a mentor myself, because I asked my partner to review my CV like five times or even more, and he's like, no god, I'm not doing this again. So I said, okay, I will come to my sister next time, uh. But now I'm thinking, okay, I will find someone. Uh, I would find a mentor who will be like passionate about helping other people with a similar um career, with similar like tech values.
Speaker 1:I would say, uh, yeah, yeah, I love that just learning from each other and gaining that confidence because you're right, sometimes you don't realize until you start speaking things through with your mentee that you're like, actually I kind of I do know what I'm talking about and I wish I'd known that several years ago as the theme of this podcast, all the things that I didn't know.
Speaker 1:But yeah, you realize when you're speaking it you're actually talking it through with a mentee that how far you've come and where you'd like to go next as well. We spoke a little bit about those that are young girls or women that are curious about tech, and you mentioned it a little bit about. They're unsure where to get started, and you mentioned like mentorship, for instance. Something that we get asked a lot on here as well is people really worry about the technical side of things and so they're kind of like where do I get started with that? Because it's just so broad and it's kind of some people worry about investing in the wrong courses that are very expensive and um, and then there might be studying the wrong thing like do you have any advice on that in terms of where to get started? That might be good to sort of dip your toes?
Speaker 2:um, yeah, that that's a very good question and that's a question that I get uh often from my mentees or from my friends. So, first things first, if you're only starting, don't buy any courses. You can start with free resources. For example, there are a lot of courses from Harvard on computer science, both on math and computer science. So I would highly recommend going into I think it's just like Harvard website and find what courses they have there for free. They are of a very good quality. Even on Coursera, if you're a student, you can ask for financial support. So they are willing to give access to different courses to people for free if you're a student and if you're in the beginning. So they're asking for some for you. To describe your financial situation, you can just say, hey, I'm a student and I I'm passionate about tech. I don't know where to start, so be really honest there and they will give you a lot of. They can give you um courses for free. So that's the first tip. Um. Probably second one um subscribe to few people uh who you really really like. Find them either on youtube, on medium, on linkedin. Read about different people's stories. Find something in common, find those kind of people. They usually post their own resources and what they recommend you to start from. So that's kind of a general advice for everyone.
Speaker 2:We're talking specifically about machine learning. So, um, my reply would be a bit more specific, but I I want to give this like uniform answer for, uh, all tech fields, because tech would be very different. It could be machine learning, which requires a lot of math, a lot of math, a lot of linear algebra, calculus, and it's better to start with fundamentals, with linear algebra, with calculus, and then start building learning machine learning algorithms on top of that. If that's web designing, which is also tech, it's very different and probably I'm not the one who can advise on web design because I've never done it myself. But you can go and find someone who's been on your podcast or on other podcasts, or someone on SheCanGo platform who is doing web design and start learning from there.
Speaker 2:But yeah, and I think, one more thing that I didn't mention. So I saw that at the very beginning of our conversation that I wanted to take a slightly different angle and highlight what I didn't know. So I think the thing that really helped me is that I didn't know anything about corporate hierarchy at the start. Well, I actually knew about that, but I didn't think about it. I didn't think about hierarchy at all. So I was setting up meetings.
Speaker 2:So I was hired as an intern because my line manager he wanted for me to document, to write a documentation of the product that was done by a third party vendor for our company. So they hired me and it turned out that that third company didn't want to share code and repository with us because they claimed that it wasn't, it was their intellectual property. However, in the contract was very vague so it was difficult to tell, like, if they can share their code or knowledge base or formulas or something. Uh, but that was my first job and I really wanted to to get done.
Speaker 2:It was during summer, I was bored, I was like I need to get something to do and those people they're saying that they're not sharing repository with me. So I cannot do my thing, I cannot document anything about this tool because I don't have anything to document. So then I went to lawyers. I asked them to share the contract with me. Then I set up a bunch of meetings with people with that third vendor and I just started setting up meetings with different people across the company, not really paying attention to their titles across the company, not really paying attention to their titles just driven by curiosity, ideas and that passion to get something done. Uh yeah, and I still live by um that rule treat everyone the same, whether it's the ceo appear, someone you're mentoring on or someone just starting out. So the attitude should always be the same Respectful, open and human.
Speaker 1:Yes, I love everything you just said, because you're right, things like that and hierarchy, you don't really, you're not taught that at university.
Speaker 1:And then you go into the workplace and it can be sometimes quite a baptism of fire as to what happens when you go into the world of work, because you think, well, I've got all the qualifications and I'm ready to go, and then you kind of learn the ropes of a company and and that really makes a difference as well as to which company you land in as well, where you're going to learn that um, and all the politics that comes with being in the workplace as well, um, and all of those, um soft skills that that come with, uh, that you have to have to go along with all of the technical skills as well, which are just just as important, um, I could keep talking to you on this topic, uh, for all afternoon, and, but we are already out of time. It's absolutely flown by. So, galana, thank you so much for coming on and having a chat with us. It's been an absolute pleasure thank you so much.
Speaker 1:It was a pleasure to have this conversation with you thank you and to everybody listening, as always, thank you so much for joining us and we hope to see you again next time.
