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Career Coaching

Season 7, episode 4 of the DataTalks.Club podcast with Lindsay McQuade

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The transcripts are edited for clarity, sometimes with AI. If you notice any incorrect information, let us know.

Alexey: This week, we'll talk about career coaching. We have a special guest today, Lindsey. Lindsey works at the Spiced Academy in Berlin, where she helps bootcamp students get hired. I think your title is Senior Career Coach, right? (1:08)

Lindsay: Yes, that's right. (1:29)

Alexey: Welcome to our event. (1:31)

Lindsay: Thank you, Alexey. Yeah. Thanks for having me. I'm happy to be here. (1:34)

Lindsay’s background

Alexey: Before we go into our main topic of career coaching, let's start with your background. Can you tell us about your career journey so far? (1:39)

Lindsay: Yeah, sure. I studied engineering. My father was an engineer, and he very much was my role model, so I was really influenced by him. Then I went into management consultancy, where I worked for 10 years. Towards the end, there was something missing, so I left. Actually, I went to live in Italy for two years with my boyfriend at the time, who is now my husband. This was a huge disrupter, I would say, and a significant thing that influenced my career. I went to live in the Dolomites. I'd gone from London to the top of a mountain. I think really what happened there was – I had two years where I was able to sort of break away from the social pressures that we tend to have that influence our choices for careers. (1:46)

Lindsay: We tend to think that we're independent thinkers and that we just make up our own minds, but often the networks that we're part of, or maybe our families, have a big influence on us. Being there, I think, that's what I let go of. I felt very free to make choices about what I did next. But I had no idea what that would be. Really, by the end of that time in Italy, I knew what it wouldn't be. I had a big, long list of things I didn't want to do. So I returned to London at this point, and then I found a coach. This was another significant point for me. This was the first time that I saw something different. (1:46)

Lindsay: I come from this really quite analytical background and here I was talking about psychology. It was new to me and it was really interesting to me. This is another thing that affects our career and it was quite random, you know? We tend to think that somehow we can plan our career – we have this “10 Point Plan” and we believe that we can reflect on it and think it all out. But, how could I ever have predicted this? I couldn't. This was the first thing that was random. The other thing is, when we're trying to work out what we want to do, we often can't think our way out of it – we need to have a new experience, and get new data that we actually can reflect on. This is what happened to me. I bumped into something that I liked the look of and also I reflected and realized “Yeah, I was more interested in people and psychology than I had first thought.” (1:46)

Lindsay: After this, I became the head of careers business school in London. This was challenging because I had never done this before and I had a team and I was supposed to know what I was doing. But I really found my place. I really liked it. I realized that education was something I liked because it was worthwhile. This had been what was missing – consulting – for me. I did a few other things in Berlin. Now, as you said, I'm doing something similar. I'm in a careers role and helping people who want to transfer into tech, either as data scientists or full stack developers. (1:46)

Spiced Academy

Alexey: Interesting. Can you tell us a few words about the school – Spiced Academy? What do you do there? (5:02)

Lindsay: Yeah. We have a full stack program that is three months long. People come from all sorts of really different backgrounds – we have musicians, artists – some people who've maybe come from a more quantitative background, and they're learning predominantly JavaScript. Then they go on to be either frontend or backend developers. Then we have a data science program where we are teaching predominantly Python and how to build machine learning models, SQL. Again, it's people who are transitioning often into data science, but also analytics, and sometimes into consulting roles. Or sometimes they’re just going back to the job they did before, but they need the data skills. (5:09)

Career coaching role

Alexey: I think this school is quite prolific now. If you take any company in Berlin, they probably have a graduate that is working there or they at least know that this school exists. So you're doing quite a good job. Thanks for doing that. Before this conversation, I wanted to do some research in order to prepare and know what kind of questions I can ask you. While I was doing that, I found an article about career coaching. (6:00)

Alexey: That article said that “a career coach helps with CV review, project and portfolio review, job search tips, interview preparation, giving advice about career switching, helping with negotiating a better offer.” This seems like a very long list to me. First of all, I wanted to ask you – do you think this is an accurate and complete list? And do you agree with this list or not? (6:00)

Lindsay: For the most part, I would say this is quite accurate. There are some things that I would actually add to it – and maybe one thing that I would take away. The thing I would take away would be, when they say “portfolio review,” this isn't something that I would do. I would not be looking at their GitHub and commenting on it. Although there would be some self-study where they could understand what a good GitHub would look like. (6:55)

Alexey: This is not something you would do, but this is something that somebody else in the bootcamp would do, right? (7:20)

Lindsay: Yeah, this would be one of the teachers that would help with it. But everything else on there, I would agree with. I think the thing maybe that's missing – one thing I find is that people are often quite negative. When people are changing their career, they can be quite dismissive and quite negative about what they've done so far in their career. They tend to actually need help to really extract out what their previous achievements have been. I think, partly, this is a legacy where we feel like we should start on a career and have this sort of linear path, which definitely is no longer the case. So I would say that they may really need help with reframing their past, in a way which helps them sell themselves in their new career, but more importantly, just makes them feel better. (7:26)

Lindsay: Generally, they don't have an objective view of what they've achieved. For example, maybe someone's come from academia and they might say, “Yeah, I just spent 10 years there, but I didn't make it.” This is the way they would frame it. But actually the truth is, the reason why they're leaving is because they have actually succeeded there for a long time but they're fed up of having to fight to get projects, because there's a diminishing number of them. So, actually, what they really want is a more stable job. They have been successful, it's just that they're now looking to do something else. I always find that it’s really quite a brave thing to do. You know, you've done this for 10 years and to make a change after – that is brave. I would say that this is the part that’s missing. (7:26)

Alexey: So it's about encouraging people to not be negative when looking at their past achievements? (9:16)

Lindsay: Exactly. Yeah. I think so. Reframing it, actually, more than encouraging. I think the other thing is just helping people get clear about exactly what job they want to do. This might sound strange, because we're at a boot camp where it's very focused – but there are still lots of other things you’ve got to think about.”Do you like working in a structured environment or unstructured? Risky, non-risky? Competitive, collaborative?” This type of thing. Actually just helping people make sure that when they get the job, that they're as happy as they can be. (9:20)

Alexey: How do you do that? How do you help people understand what they want? I guess this is something like whether they want to work in a corporate environment or in a startup, right? What kind of startup would it be? Or what kind of corporation it would be? These kinds of things, right? (9:56)

Lindsay: Yeah, it's those kinds of things. You can ask through questioning. You can also just make people reflect on this, not only in a hypothetical way, but on what's happened in the past. Then also, a bit like I was describing, you can craft some experiments. You can encourage people to try some things out in a “safe” way. First of all, to get clearer about what they want to do. But essentially, I would say what you've described is mostly what I do. (10:13)

Reframing your experience

Alexey: There is a comment from one of your former students, Anna, who says that she definitely agrees that making people feel good about themselves is very important and you are definitely good at this. I actually have a friend who was a lawyer and now he's a data scientist. I think I heard from him what you mentioned – being negative about his previous experience as a lawyer and completely neglecting it, saying, “This experience is not worth anything, let's not put it on my CV at all. Let's not mention that at all. Let's just pretend it never happened.” (10:48)

Alexey: How do you help people to not do this? Let's say we have a lawyer and this lawyer says that whatever they did in the last 10 years is worthless. How do you convince them that it's not actually worthless? That it’s actually good experience that is worth putting on a CV and that you just need to emphasize it the right way? (10:48)

Lindsay: Well, I think the first thing is to have a very honest and open conversation about what they think they failed at. So you give them the space to let all of this come out and then encourage them to think about what their successes were. Some people really struggle to do this and think “I didn't achieve anything.” So you can extract this and this sort of works as an exploration. Actually, when you do this and people think about things where they’ve said, “Oh, this is when I failed,” they start to realize that this really wasn't an objective view. This is the first thing. (11:51)

Lindsay: Then, there's the second part, which is packaging it up. The two things are related, but distinct from one another. When it comes to packaging it up, the first thing I would ask would be like, “First of all, have you worked with data?” This lawyer may or may not have. (11:51)

Alexey: Probably yes. Like, he would have to be able to find things. I don't know how they do this, because there are so many laws and then they need to locate the right one, right? That’s still data. (12:53)

Lindsay: Yes. Exactly. Just like you said, a lot of people have worked with data, but somehow haven't recognized it. They say, “Oh, but it's not what I'm doing now.” No, it's not. But let's work out what you have done. This would be the first thing – to get them to put on their CV where they have worked with data. Then the second thing would be where they have demonstrated similar skills. For example, in data science, maybe someone is an engineer and might have built simulation models. It's quite different in some respects, but there are similarities. There's some sort of evidence that it would be similar to machine learning. That would be the second thing you would do. (13:02)

Lindsay: Then the third thing would be transferable competencies like problem solving and analytical skills. “When have you done this?” A lot of people have done this already. Here, it's important to describe it in a way as achievement-based. What people tend to do is say, “Oh, yeah. I've got evidence of this.” But then the way they describe it is sort of responsibility-based. They use the language that makes sense from their previous domain or industry, and it's not always comprehensible to someone who's not in this domain. I see this all the time and everybody does this all the time. Because, of course, when you're writing it, you understand it – but you forgot what it's like to not be an expert in it. (13:02)

Alexey: So in our example the responsibilities would be something like finding, (I don't know what lawyers do, I hope I don't offend anyone) finding the right laws and things like this, right? But then, this is not an achievement, this is responsibility. “This is what you were doing” whereas an achievement would be “Helping this client get that.” Right? (14:43)

Lindsay: Yeah. I'm sure there will have been some very complex analytical things that they've had to work out. But we don't need to know the details of this and they shouldn’t use law terminology that we don't understand – we just want to extract it out. Maybe they were chosen by their manager to manage this particular client or project and during this work, they demonstrated problem solving and analytical skills. So you give evidence, but you also point out the obvious. (15:06)

Lindsay: There would be other skills, like “fast learner”. I'm sure, as a lawyer, you have to learn fast. Again, you would show this. “Communication” would be another one. There are many things that we take for granted, I'm sure, in your job and my job. Yeah, I do things that you listed out, but I also do other things that are not related to my specific expertise. We have to be rounded. I think by the time we've spent maybe two or three sessions doing this with the student, then they sort of become more comfortable and also, they're able to have produced something which is condensed into a package that someone that doesn't know can understand. (15:06)

Alexey: So each student gets two-three sessions with you to talk about all these things? (16:16)

Lindsay: Well, actually, the way we do it is – we have unlimited access. Of course, people don't come like 100 times. [laughs] If everyone did this, it wouldn’t work. There's actually myself and my colleague, Olga. There are two coaches now. But we actually work with people whenever they want, right up until they get their job. So it is unlimited and people don't overuse it, and that's why it works. (16:23)

Alexey: But typically, on average, is it like two to three sessions? Is it more or less? (16:49)

Lindsay: I think the average is maybe four. Yeah. (16:54)

Alexey: Do you come across people who do not need your help at all? Maybe they don't have any problems with finding a job? Are there people like that? (17:00)

Lindsay: There are some people, yeah. There are some people who we wouldn't see at all. Sometimes there are people who get a job easily. Sometimes there are people who we don't see at all, who maybe are the ones who need the most help – there's also this category of people. Yeah, there are some students who we would only see in a group. We also offer group sessions where we all come together. We offer five group sessions on various topics, and then one-on-ones and we might not see them in the one-on-ones. (17:09)

Helping with career problems

Alexey: How important is it, in your opinion, to have career coaches for boot camps? Let's say, what would happen if there were no career coaches in the boot camp where you're working? (17:43)

Lindsay: Well, we did have this experience once. Last year, actually, I had COVID, so I was out for a while – longer than then you would normally be with a standard virus. I think the impact would be that some people wouldn't attempt to make the move. They would maybe just return to their old jobs without really trying it. And I think this would be because they wouldn't believe that they could actually do it. I think one thing the coaches do is keep the history of what's happened to all the students. A lot of it we have in our heads – of course, we also have some data, but we're able to explain the profiles of people and what sort of role they've got after the camp. (17:58)

Lindsay: Again, it's sort of like this evidence base where we can share this and I think this helps with people's beliefs. I think other people might find it difficult to navigate the market. So they might target the wrong job, first of all, or at least take longer to work out how to do it. Worst case, it might not work at all. Some, for sure, will have CVs that are good for the job they did before, but not good for data jobs – the paperwork might be wrong and they might not get interviews. I also think some people would fall out at interviews because they're not quite sure how to explain their strengths or their weaknesses. I think it would have an impact, but I also have the faith that people somehow would find their own way. But yeah, I guess we do make a small contribution to that. (17:58)

Alexey: You mentioned a few problems. First, people sometimes don't believe in themselves and they think “Okay, I won't get this job, so I won’t try. I'll go back to whatever I was doing.” Then you also say that another problem is that their CV is not prepared for the job they need. They probably do create their CV in the old way, but they need to change it slightly. Then there are also interviews – maybe they are not prepared for them. Are there other problems that people have? (19:41)

Lindsay: Other problems. Yeah, I would think one other problem I see is that sometimes people know that they want to work with data, but they don't have a good understanding of what all the roles and possibilities are. They don't really understand the marketplace. Therefore, we would maybe do a session to help them with this. For example, there is no standard terminology yet, so what one person might call a “data scientist,” this might actually be something that another person might call an “analyst”. We do have this textbook definition, but people don't get it. This is confusing, I think, when you come into a new sector. It can be confusing anyway. But this is something data has, since it's still emerging. (20:28)

Lindsay: I was reading last year, because of COVID, there was a lower budget available for data science. As a result, the machine learning engineer role was getting merged into the data science role and therefore, people have to be able to write better code, which ends up in production – code that can perform and be stable. Then this ultimately has an impact on the junior data scientists who maybe have to be able to know more Python. So it's very difficult to navigate your way around this. There are also new roles coming up like “analytics engineer” – what is this? I'm still reading actually, I think it’s a data engineer that works in an analytics team. But this is the type of stuff that's not so obvious. (20:28)

Lindsay: Often you look at data science roles and it has the data engineering skill set on it – what is this? This is because some places can't get the data engineer, so they're asking the data scientist to do it – it's very ambiguous. So this is something people have to understand. Then I think the second issue is “Okay, now I get this, which is complex – but I don't know where I fit in here? How do I make my first move into this data world?” This is something that we would probably do on an individual basis. We may be talking about the landscape in a group and then we might speak to someone and say, “Okay, what do you already bring?” Which are some of the things we've touched on already. (20:28)

Lindsay: It's actually quite difficult for people to do that. We might say, “Okay, do you have linear algebra?” They're like, “Yeah, of course I have.” It's like, “Okay, but you have to be explicit. Put this on your CV. Do you have calculus?” “Yes.” “Also put this on your CV.” Or maybe even like a small online course. Perhaps before the bootcamp, they've done something that they think is trivial, but it's actually important. Because as a recruiter, if I look at someone's CV and I can see that they've been interested in data, actually, for the last two years and they've self-taught something, or they've done a small course – then this starts to build up a picture. So, we just then help them work out what their first move would be. (20:28)

Alexey: Yeah, it’s quite complex. Amongst these problems, what do you think is the most difficult one to help with? (23:36)

Lindsay: I think one problem that I maybe didn't mention yet, which actually is the most difficult one – I think getting students oriented around the role. Eventually they understand and they know what they want to do. They may also understand how to package themselves. But one thing that I think people struggle with when they're doing these boot camps is – I'll say “What job do you want?” and people will often say “I want to be a data scientist.” And I'll say, “Okay, but what else? What industry? What domain? Any particular technology?” and they’ll say, “I don't care. I just want to be a data scientist. I love it.” Somehow this flexibility and openness – you might say this is the best strategy and it’s going to lead to better chances of getting a job – but it actually doesn't. It doesn't work too well. (23:48)

Lindsay: If I'm a recruiter in an ecommerce place like Zalando, or whatever place, and I get a CV and a cover letter from someone and I can sort of tell they “just want to be a data scientist”. I'm going to get hundreds like this. But if I get one where they've said, “Okay, I understand the ecommerce market. I understand your business. I want to build recommender systems. I know quite a lot of machine learning doesn't make it into production, but I know this will. I want to have an impact on the business.” They have it very tailored and maybe they even have their final project as a recommender system – then I'm much more likely to interview them. But what can be difficult for people is if you genuinely don't have a focus, how do you get it? (23:48)

Finding what interests you

Alexey: I was going to ask that. (25:36)

Lindsay: [laughs] So what we would do to try to help people is – first of all, it's okay to have a mixed strategy. You can send out some stuff generically. This is fine. But the thing that works well is trying to find at least some areas of interest. We actually use a model called Ikigai. It's Japanese – “iki” means “life” and “gai” means “worth doing”. This is a model that helps you find your ultimate job that you would love. And what it suggests is to start with what the world needs. Of course, the world needs lots of things, but what you'll do if you're a data person is start to look at a very high level of the types of things that the world is asking for. (25:39)

Lindsay: You start really reading high-level trend reports. I read one recently that said, “Okay, in 2022, cybersecurity is probably going to be the most built machine learning model.” Maybe this resonates for me, or it doesn't. Or “climate change” or whatever. But what you'll find is that you will naturally be drawn to some topics that are of interest to you at a very high level. This is a good place to start. Then when you have a few of those – that are interesting to you – then you can drill down a little bit. So you say, “Okay, climate change. What use cases are there in machine learning where it's actually been applied?” (25:39)

Lindsay: And you get curious about this, you know? Then maybe you do your final project in it and you end up writing a better tailored CV, cover letter. So when you go to an interview, you have some things to say. There's more to it than this. But just to give you a flavor, this is some of the way you might start to get some sort of focus. (25:39)

Alexey: So, having focus is a good thing. That's true. I remember also – I guess, I was lucky when I was switching, because I didn't have a focus. I was ready to just work anywhere – just hire me. [laughs] (27:34)

Lindsay: [laughs] Yeah, but what's interesting is that people do see this when they're going off the bootcamp, but when you catch up with them 18 months later, they say “I didn't like it, because X, Y, and Z.” So it's not really true. It feels like this in the moment. There's this advice, of course, that you shouldn't be too picky, which is also true. But within this huge landscape of stuff that you could do, at least try to look for the thing that might work better for you. (27:50)

Alexey: I guess since most of the graduates are looking for a job in Berlin, there are many companies to choose from, right? This allows people to have this focus. It's not like there are just two companies that hire data scientists. (28:20)

Lindsay: Before I spoke to students last week, I had a look. I was looking at the number of junior roles on LinkedIn. I do this periodically. Actually, I did it when I first started and the jump in number was huge. I mean, I think for data analyst junior positions it was over 800. When I started, it was 60. I started two and a half years ago. Data scientist was a bit less – 50, I think. Data engineering was less than data analytics. This is the first time I've seen this – that there were actually more analytics jobs than in engineering. Yeah, in Berlin – but we also have campuses in other parts of Germany. (28:34)

Alexey: In 2020 when COVID hit, companies started to reduce their budgets and they stopped hiring juniors, right? But now, it's back pre-COVID – or even higher – the numbers are higher for junior people. (29:16)

Lindsay: Yeah. They’re higher and I think this is it. There's a backlog. Also my sense is that, of course, there's been a lot of digitization, so there's a lot of analytics jobs. This is why I think the order of magnitude is even higher for analytics. It maybe gets a more immediate impact sometimes on businesses. Sometimes, a lot of companies I think are still playing with machine learning. So analytics is needed by a lot of places now. There's a lot of data that we've generated, [laughs] like we're doing now. A lot of online stuff. (29:34)

Tailoring a CV and “spray and pray”

Alexey: Let’s come back to having focus when looking for a job. How much research do you think people should put into learning about companies before they apply? Should they just “spray and pray” – just apply everywhere? Or should they do a bit of research and first select a niche, and then select a few companies and learn as much as possible about them and then apply? (30:13)

Lindsay: I think there's no right answer and it depends on your situation. If you have a background that allows you to “spray and pray,” as you say, it can work. For example, say you've worked in consultancy and you want to go back into consultancy – you're going to take your new data skills with you. Then you could probably do quite a generic campaign. Maybe you don't even bother with a cover letter, you just tailor your profile and you just throw it out there. If you've done something completely different and it's gonna be a leap, then you have to make more effort, I would say. (30:37)

Lindsay: You can have a mixture of both these situations, so I don't think it's an either/or – you can have a mixture. You can have some tailored stuff and some generic things. There's no right answer, it depends on your situation. But generally, I would say that if you just sit down on LinkedIn, and you do a generic cover letter, generic CVs, and just start applying willy nilly – it's gonna take you longer. This is what I see. The job search usually becomes longer like this. (30:37)

Alexey: Okay. So it’s better to have some focus. I guess, as you said, to have some balance, people should pick a few companies to research, apply to them, tailor your CV and cover letter to these companies. Then, in the meantime, also “spray and pray,” right? (31:53)

Career coaching outside a bootcamp

Lindsay: I would also add to what you've said is – if you can, if those companies can belong to some sort of industry or domain, or even technology focus, then that's even better. Because then, you can really do some research at this high level as well and you can be quite knowledgeable. And hopefully you can get something that you're interested in, because when you get in there, you might not want to be creating recommender models for people to buy more dresses [laughs]. Maybe you want to do something else. So it makes a difference, I think. (32:11)

Alexey: We have a question from Amen. Does the Spiced Academy provide career coaching without the bootcamp? Or do you have to go to the bootcamp to get career coaching? (32:46)

Lindsay: Yeah. That's an interesting idea. [laughs] No, we don't. An interesting idea, but currently, it's not a separate service in its own right, no. (32:57)

Alexey: Do you have any advice for people? Say somebody wants to find a career coach. For example, there is a question from Michael. He's a self taught student. Do you have any suggestions for him to find a career coach? Should he actually do that? Should he even find a career coach? If yes, how would you recommend finding one? (33:07)

Lindsay: If I were him, I would probably go to LinkedIn and I would do a search there. Then I would look at the qualifications – have they actually done a qualification as a career coach? How many years of experience do they have? Also, quite often career coaches have recommendations from people. You could connect to them and actually talk to these people and see what it was like. I think also, coaches are willing to do like a quick 15-20 minutes of just chatting. (33:31)

Lindsay: Because other than just the skills, you need to have some sort of connection. I think the other thing I would do is choose one that was in the city that I wanted to work in. Probably, the sector as well. Some of it is generic, but if you're working with a career coach that doesn't know anything about data, then it's gonna be much trickier to get something. (33:31)

Alexey: So you want to have a career coach that specializes in data or in data science. And they should be in the city, or at least the region, where you want to find your job. (34:37)

Lindsay: Yeah. I think that really helps. (34:46)

Imposter syndrome

Alexey: There is quite a big topic that I also wanted to talk about, which is when people don't believe in themselves. As we talked about, this is one of the reasons that people decide not to look for a job – they just don't believe that they are good at this and they won’t find anything. I think this is called “imposter syndrome”. I think everyone has it. (34:51)

Alexey: Every month or so, I have these thoughts of “Okay, am I good enough?” Or “What I am doing – is it good enough for the company? Or not good?” Then I have to convince myself that it's actually good and try to get external feedback. So I think everyone has this. Does this problem come up a lot in your coaching sessions? (34:51)

Lindsay: Yeah, for sure. It does. It comes up a lot and I think it is a big topic. I agree with you that we can all suffer from it. To just expand on that – what can we do about it? I think it's also interesting. To answer that question, it's good to sort of understand, “How does it come about that we feel like this?” The way I might try to encourage us to think about it is – first of all, imagine for a minute that you've decided to maybe not go to a boot camp, but pretend that you've been at a boot camp. So you take your CV and you invent it. You say, “Okay, I've done a three month data science bootcamp.” When you've not. And then maybe you put your CV in and you get invited to an interview and you get someone to do the offset coding challenge. (35:41)

Alexey: Is this a thought experiment, or do you actually suggest doing this? [laughs] (36:45)

Lindsay: [laughs] No, this is a thought experiment to get better. Because it's quite complex to understand. It's quite a strange thing we do. Like you've said – you're a senior data scientist and you're doing this every month. So, how do we get to this way? (36:48)

Alexey: It’s not often. (37:01)

Lindsay: So imagine this person's got someone to do this offset coding challenge. How far are they gonna get with this? Right? I would imagine there's a good chance they're gonna get found out at the technical interview stage. But let's imagine they don't. For some reason, they don't – they make it and they're in the job. How long are they gonna survive there, in this team with senior data scientists, data engineers, product managers. We can’t really imagine they're gonna make it to Friday. So what's happening there, if you are a fraud but you're surrounded by people who basically know the topic well, you're not going to survive at the company. (37:05)

Lindsay: Isn't that quite odd when you think about it? A lot of us are spending time in situations where maybe we’ve been happily employed for years in a company, and received really good feedback, and we're still feeling these feelings of being an imposter. So what's going on there? Really, the only way to understand this is that those people that are surrounding us have some objective view about us. And that's different. Of course, their view of us is still subjective, but if we see that it's a number of people and take the point of view that it's an objective view of us, then it means that our view of ourselves – our subjective view ourselves – is distorted in some way. The only explanation is that it’s about the perception of ourselves. (37:05)

Alexey: Maybe it’s because I know much more about myself than others? (38:47)

Lindsay: Yeah, but this is the thing. So then we think, just like you said, “How can this be? I know myself better than everyone else. This is real. I feel it. It's very painful.” We've all felt it and it feels very true. Yet, we can’t really be frauds because we wouldn't survive. It's kind of paradoxical, but to feel these feelings, you actually have to be successful. You actually have to have gotten into a position where you're been employed, or you've got the job, to feel them. (38:51)

Lindsay: To understand how we end up with a self-view that is somehow distorted, you'd really need to think about “How do we form our identity?” It's not something we think about very often, right? But how do we actually form this self-view? You say that you know yourself – but do you? I would ask. [laughs] So the way we develop this is, first of all, in our families. This is our first experience of life. And they give us labels. Sometimes very overtly, sometimes subtly and unconsciously – but they tell us things about ourselves and we internalize them and we very much believe them. We've got no other choice. (38:51)

Lindsay: Then we get older and we join new networks. We go to school, for example. Say I come from a family environment where I've kind of decided “Somehow I'm not good enough in some way.” And then I go to school and the teacher tells me that I'm sort of outstanding and I get really good marks. Here I am with lots of evidence. So what do I do with these two conflicting things? At this young age, what we tend to do is give more weight to what we've been told in our family. But we have this conflict – what are we going to do with this advice that is coming from this external source? What we tend to do is find a way to dismiss it. We say, “Oh, well, I only got good results because I studied very hard.” Or “I was lucky. I actually knew what would be coming up in the exam.” And we dismiss it. (38:51)

Lindsay: The positive stuff gets dismissed and we have this belief that somehow we're not good enough. We carry this pattern on, when we go into the workplace. As soon as we receive some negative feedback – wow, this gets our attention. We believe this. And we tend to ignore the positive stuff. There's a name for this, which is “confirmation bias,” where we find what we look for. Then we can start to see how we would be doing this. So I think the first step to solving this is to actually recognize that you have it. Like you've said, you can kind of recognize that it's not true in some way. (38:51)

Lindsay: But often, if you do feel like an imposter, you don't walk around saying, “Oh, yeah. I've got imposter syndrome.” You're saying to yourself, “I'm actually not good enough and I'm keeping this quiet. I'm not telling anyone.” (38:51)

Alexey: I don't want to tell it to my manager. [laughs] It’s not something he needs to hear. (41:54)

Lindsay: No. It's very hidden. So the next step is to think “Okay, what is the trigger? What is the thing that happens before I get to those thoughts?” At work, we're often given a task. And our tasks are challenging. We're in workplaces – we're asked to do stuff that we don't always know how to do. Maybe we do this task and 80% of it, we do well. But the other 20 doesn't go so well. When we have this, you're basically like at a fork in the road – you've got two roads that you can go down. (41:57)

Lindsay: One road would be to focus on the bit that went wrong. “Oh, my goodness, how can I have done this? How can I have missed this? Thank goodness my boss wasn't there because otherwise I would be out of my ear.” So we very quickly got to feeling like an imposter. This will cause us to have certain (usually bad) coping strategies. We'll become stressed, maybe overwhelmed – it can lead to different behaviors, depending on your particular situation. But maybe you become a perfectionist, you overwork, you try to polish things. You're in this sort of space. (41:57)

Lindsay: Another way would be the other fork in the road, where we would say, “Okay. Well, actually, we'll spend some time looking at what went well. We will also look at what didn't go well, but from the point of view of seeing, what could I learn from this?” First of all, you do have to process it. It's still uncomfortable. No one likes to fail or mess up. But we can process it and think “What can I learn? What can I do differently? What are my skill gaps?” And this leads us down the road of acceptance of failure and to the fact that we can make mistakes and go on to be better. (41:57)

Lindsay: So even though we can explain this, that we have this choice of these two different roads – this happens in the blink of an eye. We'll let you know if this situation happens and we're already there. Really, what we're seeing is that we need to take time to look at ourselves, our beliefs, how that affects our thinking and ultimately our behavior. Because it is a sort of self-fulfilling prophecy. So how can we do this? (41:57)

Lindsay: There are three things I would suggest. You can get a book. There are books there on imposter syndrome. There's one, (I have it here actually, I’ll show you). It's from the 1960s. It's definitely got a 1960s look about it. It's by Dr. Pauline Rose Clance. This was the first person who coined the phrase “imposter syndrome”. In there, you'll find this. There's other books as well, where you'll find exercises that will help you try to change some of this aspect about yourself (41:57)

Alexey: What’s the name of the book? (44:53)

Lindsay: It's the Imposter Phenomenon – When success makes you feel like a fake. It's a little bit dated now, because when this was done in the 60s, she talks in here, for example, about the fact that more women than men suffer from it – which is not true. I think it was probably true then, because society was giving women the belief that they couldn’t work. Now, the research shows that it's the same – it's not gender-specific. So there are some things in there, I think that can help. (44:55)

Lindsay: The other thing you can do is work with a career coach, if you find that you can't solve it on your own. Because this is the thing, you're trying to solve your self-perception and you're likely to stand in your own way. It can be useful to have someone to help you. If you don't want to work at that level, the third piece of advice I would give you is to find a mentor. There are plenty of technical mentors out there that will help you for free, like The Mentoring Club, for example, that's one of the sites. And this person can help you. Maybe they're a senior data person. So when something is happening, you can share it and they're more likely to lead you along the path of saying, “Okay, right. What do you need to learn?” As you were saying, – your question was also about, “Does it happen often at the bootcamp?” and I would see “Yeah.” (44:55)

After bootcamp

Alexey: I'm curious. For example, for me, I've been in this industry for quite a while – I have worked as a data scientist for the last six, seven years. For quite some time. I learned how to live with this feeling. But I imagine for somebody who is just starting their career, or they've not even started yet and want to switch – they enroll in the bootcamp. Now they think, “Okay, all my experience as a lawyer is worthless.” Nowadays I look at the list of things people need to know to apply for the job, and I can see how that can lead to this feeling that “Okay, I'm not good enough. Let me go back to being a lawyer.” So What do they do? (46:21)

Lindsay: Yes, you're right. Yeah, what do you do? I would say this – when we're changing careers, this is a particularly fragile time for the imposter syndrome. As you said, in the workplace it can be even more challenging. Part of the reason for this is that, of course, we're in something new and our skills are emerging. Another thing that affects our identity is the things that we repeatedly do every day. (47:08)

Lindsay: We internalize – we develop skills and achievements, and we internalize them and these form part of our identity. So if I do maths and calculations for the last two years, then I can say, “Yeah, I'm good at maths.” But of course, when we're moving into something new, like we've done Python for three months, we're not going to feel confident in it. So I think there's an acceptance that this is the case. And I think the thing is – it’s the same thing with these forks – so we can decide, “Okay, when I apply for a job, this means I'm an imposter. So actually, I won't bother.” Or, we can say “It’s emerging. I've actually found something I enjoy.” And this is a really important thing, because I meet people who don't ever find this. (47:08)

Lindsay: If you find something that you enjoy doing, and you can see that you could get good at it – then this is enough. You're on the second path of thinking, “Okay, I'm developing it.” I think this is the second challenge about moving into a career in tech. Other sectors are like this, but tech, particularly in data science, even more specifically – is that there's so much to learn, first of all. Secondly, there's not an obvious structural path. I think I told you, my dad was an engineer, if I think of his career paths – he spent four years learning it. It was really structured. Then he did an apprenticeship. Then he spent 20 years, and it was very structured. (47:08)

Lindsay: I think one thing that can really help when you come off a bootcamp is to say, “Okay. When I finish, what are my gaps between where I am now and what do I need to get for the first Junior role?” You can find this out by asking people who've already done it. So I would go to a place like LinkedIn and I would find a lawyer, who then became a data scientist, and I would connect and say “Tell me – how did you do it?” You can create your learning path from where you are to where you need to get to. I think the other thing is, once you get into a job, to then expand this. I would find someone within the firm and say “Right. I want to have a high level picture.” Even if it's something that you're not going to learn for the next couple of years, it will help you. This is the thing when you get the job – this helps. You know you got your first job, so you start to really believe it. (47:08)

Lindsay: Also, a part of our identity is feedback from other people. I cannot decide, for example, that I'm sporty if nobody else in the world agrees with me. We also make our identity based on the direct feedback we get from others. Say you're in a meeting in the first few months and people are all talking about some technology, and you have no clue what they're talking about, then this feeling is going to be really strong. But if you're able to say, “Okay, this technology – it's a data engineering technology. I can see it on my plan over here – on my Learning Plan. I'm going to get to it.” Then this helps. (47:08)

Lindsay: I think the other thing that helps is to think about building your skills, in terms of a T shape, where the top bar is the width. You know that there's a certain number of topics that you need to cover in your role and you won't be able to do them all in depth. But you work out “What is the depth gonna be?” I think this is quite anchoring. If you have at least one topic – and as you get more senior, there'll be more than one topic – something that you know really well, then I think that helps. (47:08)

Lindsay: The other thing that I think helps when you're coming off a bootcamp and, as you say, you're changing for the first time is getting clear about what is the expectation of the role. Because I find that students, of course, they're not clear on this – we've just explained how muddy the marketplace is – but they tend to exaggerate it. They'll say, “Look, I've got no chance.” And they'll show me a job ad and say, “I need a PhD in statistics to be a data scientist.” And I was like, “Okay, so you're not going to do this role. But there are plenty of roles where you just need a linear regression. And if you don't know how to do this, then you can go and do some data analytics for a while, or maybe even be a business intelligence analyst and just do SQL for six months. (51:10)

Lindsay: There's always a way. I think, again, by connecting and talking to people – no one will give you the same answer, since there is no one answer – but you will start to see that what's expected of you at a junior level is achievable. So working on your inside and understanding the outside – you will get there. I see it happen every day – hundreds and hundreds and hundreds of people get there. (51:10)


Alexey: Yeah. We have quite a few questions. The first question I see is about internships – we have internships and we have junior positions. So do you see your students applying for internships or is it only for university students? Is it even advisable to do internships or should they aim for permanent roles? (52:25)

Lindsay: This is a good question. And it depends on the person. So yes, for sure, there are some students who do internships. First of all, the number of internships that's available compared to juniors is really small. So the ones that you'll find in LinkedIn – I don't know how many there are today – but it can be a really small number. Whether you need it depends on your situation – so if you are a structural engineer, you have all the maths, you already coded a bit and you're gonna go for a data science position at a structural engineering firm – then no. You can just probably go straight in. But if you don't have this – you're not bringing a lot of that with you, then yeah, why not? If you can do it financially. (52:51)

Lindsay: It can be a good in. I can think of one student who did this. It also depends on the role, like, if you want to be a data engineer, it's difficult to just walk into a junior data engineering role. But I've seen some students go into a six month internship, and then they've got a junior role. This is quite a quick way to get it. So I think it depends on the individual. But for sure, I think it's a great route. (52:51)

Lindsay: The other thing I wanted to just say on this, as I said before, you won't necessarily find a lot of them advertised in LinkedIn, but you can generate them through networking. I see a lot of students connecting and the person. They don't call it an internship, but they say “You come into my team for six months. If it works out, then I'll make a position for you.” So you can also generate opportunities that maybe aren’t even actually there. (52:51)

Alexey: Yeah, actually with internships, it's tricky. Some of the internships we had, we closed them without “going public” with them, let's say. Because usually the moment we publish it, it’s just too difficult to deal with the amount of applications. That's why we ask “Hey, do you know anybody who is looking for an internship?” And maybe we have one position. This is how I found an intern when I was looking for one. It was through smaller communities, without advertising this publicly on LinkedIn. So this is a good suggestion to connect to people and find these kinds of things. (54:34)

Working with recruiters

Alexey: Another question we have is, “What advice would you give for working with recruiters?” (55:15)

Lindsay: So this depends on what level you are. If you're looking for a junior role, they wouldn't have junior positions, but they still might be willing to speak to you. Because once you get into data, you've kind of got a job for life. At some point, you might become their client. If you are more senior, I would say – find a good one and stick with them. That's what people do. [laughs] (55:20)

Lindsay: How do you find a good one? Word of mouth, but also, you may find someone that's like boutique – specialized in data – they tend to do salary reports. There aren't so many, actually. There's a few in Germany and quite a lot of them are London-based. I have a list, I think, of about eight that I would maybe recommend. Maybe I can share it with you if people want a list. (55:20)

Alexey: I know only one that’s Germany-based. Most of them are from the UK. I don't know why that's the case – why UK companies hire in Germany. But this is interesting and this is what we usually see. When I get a call from a number that starts with +44, I know that it's a recruiter. (56:11)

Lindsay: That's interesting. Yeah, I've seen that. In terms of how to handle them – I think that's the main way to handle them. If you get a good one, then they can be really helpful. And if they're not, [laughs] there’s just no point in actually having a conversation. (56:31)

Alexey: Have you seen your students succeed with a recruiter? Are they helpful? Or maybe students shouldn't waste their time with a recruiter and instead apply directly to companies? (56:45)

Lindsay: Well, almost all of our shoots are going for junior roles and recruiters get paid a fee. There are very few that I can think of – maybe a handful of people who've actually got a job through a recruiter. Sometimes they get found on LinkedIn by recruiters. This can sometimes happen. But it's more the exception than the rule. They don't routinely phone them up. (57:00)

Lindsay: But one thing you could do – if I were a student, I would phone them up and say, “Hey, I'm a junior. Tell me, which companies are hurting for juniors right now? What's happening?” They will share this with you because a lot of them think about the long term. Some might not respond. We do actually have someone who comes in from Orange Quarter and talks to our students – Thomas, he's particularly good. (57:00)

Alexey: Orange quarter is the name of the company? (57:52)

Lindsay: Yes, it’s the name of the company – a UK company. They've got a lot of information about salaries, what's happening right now, which sectors are recruiting. So I would take advantage. More from this angle at the junior level – they don't have Junior roles. (57:55)

Networking on LinkedIn

Alexey: Yeah, thanks. Maybe the last question, because I see we're almost out of time. If you can answer this quickly, because this may be more of a complex question that it looks like. So the question is “Can you give some tips for networking on LinkedIn?” (58:12)

Lindsay: Okay, so quickly. If I tried to do it quickly, I would say – send a note that's gonna encourage the person to connect to you. (58:30)

Alexey: Okay. So when you connect, add a note. (58:40)

Lindsay: So, how can you add a good note to someone who's got a similar background as you? For example, “I'm Scottish. I am a mechanical engineer. I want to be a data scientist.” To someone from that profile, they're more likely to help you if they're similar to you. Make it like a little, tiny mini-cover letter. Make yourself credible and just ask an informational question. Something that you really want, like “How did you make your career move?” Start gentle and then from this, you will develop into something. Reframe it – make it about asking questions that you're trying to get the answers to and don't feel that you're networking. It'll happen naturally. (58:44)

Alexey: Okay, yeah. Thanks a lot. Thanks for your time. We should be wrapping up. Thanks a lot for sharing all this advice with us. And thanks, everyone, especially Michael, who needed to wake up at 6am to watch this. [laughs] I hope it was worth your while. So yeah – thanks. (59:24)

Lindsay: Thank you so much, Alexey. It was good to see you. (59:44)

Alexey: Likewise. Have a great rest of your day. Goodbye. (59:47)

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