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Accelerating The Job Hunt for The Perfect Job in Tech

Season 17, episode 6 of the DataTalks.Club podcast with Sarah Mestiri

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Alexey: This week, we'll talk about accelerating the job hunt for the perfect job in tech. Today, we have a special guest, Sarah. Sarah is a data scientist and a certified career and interview coach. She has over six years of experience working in tech, working at international companies, financial companies, and startups. She went through a career transition herself and she also wants to support women (other women) in following their professional dreams. (2:51)

Alexey: That inspired her to become a certified career coach. She is on a mission to support more women to find the right job for them. As a career coach, she has been supporting women getting back to work after a career break, and women in transition to the data field. Today will probably talk more about that, and in general, about finding the perfect job. Welcome to our interview, Sarah. (2:51)

Sarah: Thank you, Alexey, for having me a guest on your channel. I'm very pleased to be part of it. (3:52)

Sarah’s background

Alexey: Yeah, thanks. Thanks for accepting the invite. The questions for today's interview were prepared by Johanna Bayer. Thanks, Johanna, for your help. Let's start. Before we go into our main topic of finding a good job, let's start with your background. I already mentioned a couple of things but maybe you can tell us more about your career journeys so far? (4:00)

Sarah: Yeah, sure. I have a background in computer science engineering. I studied in Tunisia before moving to Berlin. I worked for a few years as a full stack developer before transitioning to data science, as you said – I made a career transition myself. Since 2018, I have been working as a data scientist at REMERGE, which is a company for mobile app retargeting. It helps people use mobile apps again by displaying ads. (4:24)

Alexey: I have already worked in the ad tech space but correct me if I'm wrong. Let's say I play a game, and then something happens, and I forget about this game – I stop playing it. Then what you do is you see me in some other app and you say, “Hey, do you remember this game that you played? Come back!” (5:13)

Sarah: Exactly. Yeah, that's retargeting. That's the main area where REMERGE started. We also started user acquisition recently (last year). That's where I started in data science and still work at REMERGE. At the same time, after having my first child, I decided… I was like, “Okay, I want to do something during my parental leave,” and so on. Because I needed to find something for myself. That's where I started the journey into my own project, which is Thriving Career Moms, where I started helping moms returning to work after maternity. Then, until recently, I also started to support women in tech, or women in career transitions, so not necessarily only moms. Yeah, that's a little bit more. [chuckles] If you have a question about my journey, then [ask]. (5:39)

How Sarah became a coach and found her niche

Alexey: How exactly did you realize, “Okay, not only do I want to help women get back to work after a break, but I’m also deciding to become a coach.”? What did you do? From the moment you realized, “Okay, I have some knowledge about that, and I want to share it,” to actually get in your first mentee? What was that journey like? (6:45)

Sarah: Yeah. The secret there is – I have been having the support of a coach along my journey. When I decided to transition to data science, I joined a community. There, I got the support on how to go through my career transition. At the same time… Also, another time, when I decided to start my own project, I got the help of that same community, which is Classy Career Girl. I followed the steps. I was like, “I’ll take the first action and then – okay, here's some opportunity. Let me have a membership.” And then “Okay, not a membership. Let's do something else.” So I kept trying, doing different things, and with the support of my coach, through the support of my community, where I belong, I could really come up with a product or service and start career coaching and having my own mentees. Yeah. (7:14)

Alexey: When it comes to figuring out what the niche where you can help is – at the beginning, did you already know what exactly you want to help with? Or was it something you needed to work out and understand and eventually…? (8:18)

Sarah: Yeah, I understand. (8:36)

Alexey: So you said “Okay, I want to help moms.” Right? (8:38)

Sarah: Yeah. I think that’s a process that I'm still going through, even today, because you keep [thinking], “Okay, now is this the right niche? Is this?” However, I knew that I wanted to support women from the beginning. For example, I want to support other women because I have my [challenges] from my own journey – I know that we face certain challenges. I feel called to support women. But then, okay, is it moms? Is it women in tech? Is it women transitioning to data? I'm still working on this step by step, with small steps. [chuckles] (8:40)

Sarah’s clients

Alexey: What are the most common things that your clients come to you with? What kind of problems do they have that they need your help with? (9:27)

Sarah: Usually, they are looking for a new job or I also help those who started a new job – so they started a new job, but they don't have a specific goal. I supported them in the first month. But most of them are career changers and looking for a new job. (9:42)

Alexey: Which is exactly the topic for today's interview. (10:05)

Sarah: Yeah, yeah. We talked about that in our networking lunch. That's why the idea came forth. [chuckles] (10:07)

Alexey: For those… There is a bit of a story. We met, I think it was in November, in Berlin. At DataTalks.Club, we have regular meetings – breakfasts, lunches, and dinners – and we met at one of these lunches (one of these meetings). Interestingly, along with Sarah, there was her client. (10:16)

Sarah: Yeah. [chuckles] (10:42)

Alexey: [chuckles] That, for me, was super interesting. That's why we are talking [about that] right now. It took some time to arrange everything. It was a really interesting lunch. (10:43)

Sarah: It's also a career transfer to data. (10:56)

How Sarah helps her clients find the perfect job

Alexey: Yeah. Clients come to you and ask you to help them with finding a new job. So what happens after you have the first call? How exactly do you help them to find this perfect job for their ambitions? (10:59)

Sarah: Okay. So the way I help them is by taking them through the process, which consists of four pillars. The first pillar is about the job search, so helping them set clear goals about their ideal job description. We see jobs online, but which jobs are you really interested in doing? What problems are you interested in solving? What's your vision for the near future? That's the thing that we go through first. Then, it's about having a networking strategy and knowing which people you are going to reach out to, so that you increase your chances for a referral. [It helps you to] know about the right opportunities. (11:19)

Sarah: Then, it's about the resume and interview preparation, which is about how to tailor your resume, how to use the correct keywords, and how to show the value that you provide to the company. Then, how can you express that in interviews? This means preparing for the top questions. For example, “Why are you applying to this company? What makes you interested in applying here? Why are you leaving your job? Why did you change your career?” And so on. Then, the fourth pillar is the job search strategy, which is about the main job search activities. That one is going to enter an early stage job. (11:19)

Alexey: This sounds like it's not really specific to data science—it applies to all sorts of jobs, right? [Sarah agrees] If we talk about data, this can be data analysts, data engineers, whatever, right? It's very broad. Four pillars. Okay. You said the first pillar is job search… Okay, four pillars: job search, networking strategy, CV and interview preparation and job search strategy… [chuckles] Are the first and the last one the same? (13:16)

Sarah: Okay. [chuckles] Let's… [chuckles] I’m just wondering, because I think those are the questions that… I don't know if you have the same document that I have, just because I remember that I changed it a little bit so we can really focus on the… (13:51)

Alexey: Okay, I'm a bit… I’m not following the document. For those who edit this episode later, please cut this out. [chuckles] For those listening, bear with us. So, you prefer to follow the questions, right? (14:15)

Sarah: Yeah. [chuckles] (14:28)

Alexey: Yeah. Okay. So, we talked about these four pillars right. You said that the first thing is job search, right? You need to have a clear idea… You need to know what your ideal job is. (14:30)

Sarah: The first pillar is the job description, yeah. The ideal job. (14:44)

Alexey: And which jobs are interesting for you – where you want to be in the future. So how do you know whether a job description is 100% aligned with my ambition? [How do you know whether] this job that I'm looking at right now is right for me? (14:47)

Sarah: I think that's one thing that all my clients who come to me think about. They know what they are looking for. For example, to be concrete, let's say I'm looking for a data scientist job. But then, data scientists can do many things, right? Even the titles are different. You can find for example, a “decision making scientist,” you can find an “applied research scientist,” and all of them can be put under “data science”. So, the first step that I help them to figure out is that and how to know whether this is the right alignment. (15:07)

Sarah: [We solve this] by knowing what skills you want to develop right now. You first go about this by researching. What we do is first research all these data science jobs. You look at an online advertisement, and then find some tasks that you enjoy – that you're interested in doing. We start to collect them, “Okay, I like to do these tasks and these I don’t. I'm interested in learning about this technology, and not this technology.” This is part of researching the job description. But it’s not only that. The other thing is connecting with people who are [already] doing the job. So let's say I found… the last time I found Flink, so that's my example. [chuckles] Flink had a job advertisement for a data scientist, and I see whether there are any people that I can connect with who work at the company, or are currently working at Flink. (15:07)

Sarah: Then I can ask them, “How is it to work there? What do you do on a day-to-day basis? This helps to discover the alignment, exactly – by going through the process of researching, talking, and doing informational interviews. This is a strategy that I really like because it gives you another perspective. I used it myself when I was doing my career transition. Even as I look at how I want to grow further in my position, I do informational interviews with different people so that I can figure out what the thing that is right for me would be. (15:07)

Alexey: I'm sidetracking a bit again. (17:47)

Sarah: Yes, it's fine. [chuckles] (17:51)

Finding a specialization

Alexey: I hope it’s fine. [chuckles] The problem that I personally have… Not me personally, but the problem that… Let me take a step back. In our community, we have courses. Right now we have three courses – three different courses – ML engineering course, data engineering course, and ML Ops course. (17:52)

Alexey: While the first and the last one are kind of similar, the data engineering course is quite different. It's a different kind of job, right? So people take all these courses – they take the data engineering course, the ML engineering course, and they say, “Okay, I like all the courses.” (17:52)

Sarah: All of them. (18:27)

Alexey: All of them. (18:29)

Sarah: What's the third? ML engineering, data engineering and the third? (18:30)

Alexey: ML Ops. (18:33)

Sarah: ML Ops, okay. (18:35)

Alexey: Then the students come and say, “We took the courses, and we like all of them. I want to do ALL of this stuff.” And this is a lot. You cannot… You can, but it's difficult to be a jack of all trades, right? Especially if you're on the start in your career. You need to figure out from these directions, where exactly you want to go. (18:36)

Alexey: What sometimes happens is, the students just apply to everything. They apply to data engineering positions, ML Ops positions, ML engineering positions – all of them. I am not sure if it's a good thing or a bad thing. Maybe you can tell us. But I think one of the consequences of that is that you kind of lose focus, right? (18:36)

Sarah: Yeah, exactly. (19:24)

Alexey: So how, in this situation, when there are so many options… If I try to focus them, it means that I am kind of… How do I say this? Losing opportunities. I have this fear of missing out, that if I don't apply to data engineering positions, then there’s a very big chunk of potential jobs that I will not get, because I don't apply to them. Right? If I only apply to ML engineering positions. In this case, how can I actually understand which one is better, and what is better for me? (19:26)

Sarah: Yeah. The first thing. Taking all the courses to explore is good because one doesn't have experience in the field, so exploring different specializations is a good way to start. But what I would suggest doing before starting the courses… I will talk about those who already did the courses. [chuckles] But before doing the courses and starting to apply for jobs – take some time and reflect on your profile. What skills do you have? What do you have as strengths? What do you have as qualifications, of course? Qualifications, strengths, skills – and then, what experience do you have? Taking all of these into consideration, in addition to them, your interests as well. (20:01)

Sarah: This allows you to better leverage, “Okay, I have my profile. Now, which one of these – data engineering, ML Ops, or ML engineer – would be the one that works for me?” First, I would be applying the skills that I would enjoy, and I can become good at them because I enjoy doing this. Then I can become good at it. So which ones fit better based on my experience as well? So it’s a combination of your profile – what makes you unique? What makes you, you? And then, from there, let's research these positions. What does a data engineer do? What does ML Ops do? What does an ML engineer do? Once I do that, then I choose one of these. Now it's time to address, “Okay, but I'm not sure yet. I don't have much experience in it. All of them sound interesting. I want to work with data.” (20:01)

Sarah: Many people say “I just want to work with data. I’m interested in working with data.” What I would advise them to do is to still try to choose a focus. Okay, choose a focus, but how do you choose it? Try to look at the job market. If all of them are interesting to you, then look at the job market. What does the job market say? Are they hiring data engineers more? Are they hiring more ML Ops? What's your target salary? The target environment? Study it from the point of the job market. This will help to filter. For example, “From those, it sounds like I like the data engineering [jobs], with my skills and my profile, what I want to do in the work environment, the job market, and so on – this one fits better.” From there, choose a focus. Now I want to answer the question of why a focus is not going to make you lose opportunities. Because when you focus, you simply give yourself the opportunity to grow your skills in that specific area. A mistake I made myself when I decided to change to data science – I have the background of a full stack developer and I worked with Java, I worked with backend, frontend… And I decided to move to Berlin. (20:01)

Sarah: I knew I wanted to be in data science, but it still wasn't clear for me. Okay, data science, ML engineer, data scientist? It wasn't clear at that time to me. But what I did in the beginning was start applying for all jobs – full stack developer, back end developer, backend engineer, software engineer, data scientist, ML engineer… And I got overwhelmed with, “How can I prepare for the interviews for all of these? They have different requirements. A data scientist job would require other skills compared to a full-stack developer. A data engineer would require more skills than a data scientist. Although they can have intersection samples, the main focus would be different from data science data engineering [roles].” So I was getting lost. I was not getting any opportunities to have interviews – rejections, “What can I do?” and so on, until I narrowed down my focus. That's when I started to become more confident, more ready for the interviews. I could have a specific plan for learning. And I started to get interviews. (20:01)

Sarah: I narrowed it down, I was like “data scientist” and I also remember “ML engineer”. I kept it as a data scientist and ML engineer, for me. And that was my focus. The focus doesn't really reduce your chances. Yeah. [chuckles] And another thing. [chuckles] Another thing is that companies want some people that have experience. If you keep applying for all of these, in the end, you are applying for data engineer, ML engineer, data scientist, then the company who is hiring for a data engineer, which requires more infrastructure and tooling and preparation of the data workflow and so on – they will not find you having that practice of data engineering. Because you are trying to do all of them. (20:01)

Alexey: So this means that… Let's say somebody wants to take our courses – they should do some of this work even before they take courses, right? (26:28)

Sarah: Yeah. Yeah, exactly. (26:36)

Alexey: Then you have more focus from the very beginning and you know, “Okay, I want to focus on data engineering so I'll take data engineering.” Then, after the course, perhaps, instead of taking the other courses, focus on developing the skills in this area – focus on doing more projects in this area. [chuckles] And then get interviews in that area, and try to understand where you stand, what else is required to get the job. Hopefully, after finishing our course, you get all the skills. [Sarah agrees] But sometimes, some companies might require something specific that we don't cover. This is where you can understand that and then try to focus on building a project with this particular skill. (26:38)

Sarah: Yeah. It's important not to get lost in doing all the courses. I don't know if all of them really do all the courses. Before starting a new course, just take some time. Maybe do a project. So, say I finished data engineering, and I do a project. I do a project and I see if I enjoy doing it. I suppose the hypothesis is something that I would like. Okay, I finished the data engineering [course], let me practice. Let me show that I developed skills in data engineering and then think about another course. But going from one course to another is… I don’t know the word. [chuckles] But it's better to avoid it. It’s like a circle – you get on the train, course after course after course, I feel like I still don't have enough qualifications. But remember, you also have skills, you have strengths, you have experience. So don't forget about that. (27:24)

Alexey: When it comes to having even more focus – you mentioned that, when it comes to data science, it's such a broad topic that it can mean anything from analysts to hardcore deep learning whatever, right? When you read one job description for a data scientist, it can be super different from another description of a data scientist. It’s the same thing with data engineers. (28:33)

Alexey: You can be a data engineer that builds a data platform, you can be a data engineer that helps analysts, you can be a data engineer that works with data scientists – there are also specializations.[Sarah agrees] If I already know, “Okay, I want to do data science,” or, “I want to do data engineering.” Should I already have even more focus and think, “Okay, I want to focus on this particular type of data science job.”? Or is it okay to be rather broad at this point? (28:33)

Sarah: Okay. Yeah. If you choose to focus on data engineering, there are different demands from company to company. Another thing that is in the first pillar that I've talked about, which is the job description, is not only getting a clear [understanding] about that job description and showing the things that you want to do, the title that you want (target titles), but also the companies. What are the companies that you want to work with? Choose the companies and see… The same thing – this is [what you get] by doing research – informational interview, researching. From there, you can come up with a list of, let's say, 5–10 companies maximum that you’re… I will say a list of five is enough, where you can say, “These are my top five companies that I want to work with.” (29:35)

Sarah: From there, you see, “Okay, for these companies, what are the profiles of the data scientists there? Can I network with them? Okay, let me get closer to know what they are doing (informational interviews) and see what problems they are trying to solve.” The team of data engineers, data scientists – or the company. Because every company shares its mission, its purpose, what they are doing, this is the product, and so on. So there is already a lot of information available publicly. Publicly, there is a lot of information available, so use that information in order to help you know what projects you need to practice. What are the problems that you need to practice solving and coming up with practical experience in those problems? And then, also networking with the people who worked at this company. Because that's another layer in the job search, which is networking. Does that answer your question? (29:35)

Alexey: Yeah, it does. You mentioned the word “networking” like 10 times by now so I think we should talk about that now. [chuckles] (31:40)

Sarah: [chuckles] What I mentioned 10 times? The company's… (31:47)

Alexey: Networking – the word “network”. (31:51)

Sarah: Networking! Ah, yeah. (31:53)

Informational interviews

Alexey: It's one of the pillars. But even before we go to this networking pillar, it's also something you do to realize what the perfect job for you is. Right? You know, “Okay, this is the area where I want to focus.” In order to do that, you mentioned these information interviews. (31:54)

Sarah: Informational interviews. (32:15)

Alexey: Informational interviews, exactly. So what does it look like? Okay, I have these top five companies where I want to work. I opened LinkedIn. I say, ‘data scientists at Company X (REMERGE, whatever)’.” And then I see, “Okay, these are the people who work there.” What happens next? Do I just… They don't know me. I don't know them. What exactly is… (32:17)

Sarah: At first, especially for doing it the first time, it’s uncomfortable to do the informational interviews. Yeah, it's normal to feel uncomfortable. But think about the results – the outcome. The outcome of the informational interview gives you an idea, as I said, about what the company is doing, what the role is doing, and you can get advice. Now I have that goal. I'm up for it. This is a tool that will give me access to this information. But not only that, we have to know that people like to help others. They enjoy giving advice, helping others. I don't know – Alexey, do you like helping others? [chuckles] (32:43)

Alexey: Yeah, when somebody says, “Okay, I have this problem. How about we meet for lunch?” I would not decline. Why would I say no to lunch, right? (33:29)

Sarah: Yeah. Or at least if you don't have time for lunch, you would say, “Okay, I can’t do lunch, but maybe we can have some time to talk.” People generally like to help, so to approach [them] for an informational interview, just be specific. Talk to the person. I reach out to the person, saying, “Hi.” Before reaching out to them, research that person. Research them on LinkedIn, I would say. Of course, that's the platform that we have. Do they have… [cross-talk] (33:41)

Alexey: If they’re on Facebook and Instagram, right? [chuckles] (34:12)

Sarah: Yeah. [chuckles] Professional. But I'm talking about LinkedIn. Maybe they do share some things from their work, their interests, the things that they support — I mean, missions that they support – they blog about their tech stack or something like this. So research them and find something to connect with that person through – something about them, where you can tell them, for example, “I liked what you shared here,” or, “We come from the same (something).” Connect similarities. (34:18)

Sarah: From that, say that you are currently looking for a job and working on your target companies or your target role and you are interested (you are considering) working at this company. You would like to hear from them, what advice would they give you, and how is it like to work at this company? I'd invite them for a short call, like 20 minutes, 15 minutes. Make it easy to say “Yes”. Lunch is good. [chuckles] If they say like, “Okay, let's do lunch.” But don't ask for lunch at the beginning. It's like, “15-20 minutes call or coffee?” And that's it. Make it easy to say “Yes”. (34:18)

Alexey: Well, I assume I need to be prepared when I go to this meeting, right? So what kind of questions do I prepare in advance? Do I also send these questions in advance to that person? How do you go about the actual strategy? (35:53)

Sarah: Okay. [chuckles] The strategy is — you don't send them the questions. Prepare your question. Don't send them the questions but prepare your questions. The questions could be… It depends. The informational interview can be used to know about the company or the role. So there are two things. If you want to know about the role, then focus your interview questions on the role. The questions could be like, “What do you do on a day-to-day basis? What do you find challenging in your job? What do you like in your job?” And then… (36:10)

Alexey: “What do you dislike?” Right? (36:54)

Sarah: Yeah, “challenging”. I call it “challenging,” not “dislike,” but it could be “dislike." But another question that’s good is, “What would make a person successful in [this] role?” For example, “In your opinion, what would make a data engineer successful?” These are a few questions – four or five questions, because you only have 20 minutes to ask about the role. About the company would be something else, but similar, “What do you like about the company? What do you wish was different before you start? One improvement you want to see in the company?” So not only about what you like, what you dislike, but also deeper in questions. (36:56)

Building a connection for mutual benefit

Alexey: And what can I offer in return? [Sarah chuckles] I had a few lunches and then, people asked me questions, and then I answered them. At the end, I get this question, “What can I help you with? What can I offer in return?” And my mind always goes blank. I don't know what to answer. [Sarah chuckles] I understand that, “Okay, they got something from me and they want to return the favor.” Right? [Sarah agrees] If I'm asking that person — I asked them questions, they already spent half an hour of my time. How can I figure out how I can be useful? Or I don't need to think about this? (37:52)

Sarah: Just ask. “Is there anything I can do to help you as well?” Most often, they will say, “Oh, no, thank you.” But just be willing to help. When you have that informational interview, they get to know you a little bit. For example, I talked with someone for an informational interview to ask her about her role, and she knew that I'm a career coach. So when I asked her, “Okay, how can I help you?” She said, “Oh, I might need a career coach in the future. Let’s just stay in touch. Usually, this person will stay in touch – will connect with you on LinkedIn later, after this interview. That, on its own – later, they can ask you for help. So you don't have to return that help right away. (38:38)

Sarah: Be willing to offer it and know that you have not gained a new connection, a new relationship, that can benefit both sides in the future. Informational interview is not to ask about a job. It's not to ask, “Are you currently hiring? I want to apply.” They can suggest it themselves. I know my coach said because she was so prepared for her informational interview, that the person she interviewed told her, “If you want to apply, then tell me. I will refer you.” Don’t ask it explicitly, but it can happen. (38:38)

Alexey: Also, at this stage, I guess the purpose is to figure out what you want. [Sarah agrees] Maybe you don't even know if you actually want to be a data engineer at this particular company. When you ask them, “What do you not like about your job?” And then when you hear back what they say, you think, “Hmm.. Maybe, I’ll do [something else].” (40:24)

Sarah: [chuckles] I did a couple of informational interviews and was like, “Oh, I like this!” And then the second, the third, “Okay, no. This is not for me.” [chuckles] (40:46)

Alexey: Exactly. So at this stage, it’s just getting clear on finding the right job for you. Right? (40:56)

Sarah: Yeah, but I said that you shouldn’t ask for a job because usually – even people who are watching right now and are currently in the job search – they will say, “Okay, I want to do something to get a job, not to learn about it.” [cross-talk] (41:05)

The networking strategy

Alexey: That’s the second pillar, right? The networking strategy. We kind of already started with networking. So we already reached out to people, we established these connections. But what about this networking strategy? Now I'm looking for a job. Now, I found that data engineering is the right job for me. I know the good things about this, I also know the challenges (the not-so-good things). And now I understand, “Okay, I think it's for me. Now I’ll start looking for a job.” So, when it comes to networking, how is it different from what we discussed? (41:17)

Sarah: How is it different? It's not about networking with just anyone, but networking with the right people. How do you know the right people? Who are the right people? Now you are clear on the job. I will suppose that you know the job, you know the companies that you want to work with. Now, who are the right companies? Based on that, I will find who the people I have to connect with are. But not only that – assess your network, your current network. Where are you underinvested? Where are you overinvested? Maybe you are connected to many people in another field, but not the data engineering field. (41:57)

Sarah: So, make an assessment of where you are overinvested or underinvested. From there, create a list of the people that you are going to reach out to and have a goal. For example, “Every week, reach out to five new people and connect to five people that you already know.” So networking is two things: new people and people that you already know. Why? Because actually, there is a research that was done a very long time ago by Harvard sociologist Mark Granovetter, who studied hundreds of professionals. They found that more than half of [subjects] learned about positions through personal contacts. And out of those who learned about the positions from personal contacts, only 16% of them saw that contact often. And more than 55% saw that contact only occasionally. (41:57)

Sarah: This means the weak connections (people that you don't see that often, people that know you, but only occasionally) are actually… You can tap into them to know about the jobs. Because your strongest connections are probably in your same environment – in the same network. However, those weak connections can refer you to some new opportunities. That's why, as part of your plan, connect with the right people, new people, and as well, reach out to those people that you already know but haven't been in touch for so long. Share your story with them, what you are doing right now, and just connect with them and share with them. If they know anything, then ask them if they know any opportunity and ask them to share with you. Or have a chat, have coffee with them, connect with them again. Yeah. So that's part of the networking. (41:57)

Listing your projects in the CV

Alexey: I realized that we have a lot of questions from the audience. It's actually 10. I don't know if we will be able to cover all of them, but we can start. I'll start with the first question, “I left my job two years ago, and I started learning data science and Python. How should I put the things I learned in the CV and the projects I made in the CV?” (45:05)

Sarah: Okay, I see that. “I left my job two years ago and started learning.” Okay, you can put them in because you left your job two years ago. Right now, you don't have current experience. So I would first put the skills in my resume. After the summary, I would put my skills, and then I would put my practical projects, and then experience – to make the projects the first thing that they see, the projects that you're doing. That's how I would recommend it. (45:31)

Sarah: Another thing that I did myself – because I also left my job when I transitioned into data science – instead of moving the section of practical projects, I kept the professional experience, but I added “My last job ended in this year, and from that time, I'm doing self-education.” So I put it in the professional experience as well. “Self-education, self-employment,” and I mentioned some of the projects that I did. That's also another way to do it. The goal here is to make it the first thing that they see, that, “I have experience in this field, and these are my projects.” (45:31)

The importance of doing research yourself and establishing your interests

Alexey: Thank you. Another question, “Do you know any service that can help with doing research – on companies, vacancies, and requirements?” I think this is what we talked about and you said, “You should come up with the top five companies that you think you will like. Then look at the job descriptions there and understand the requirements.” If I understand the question correctly, it asks if it’s possible to outsource that. (47:02)

Sarah: Yeah. [chuckles] Okay, that's not possible. You have to do it yourself. But I think what they're asking is about a service – maybe a platform where you can research a company. Anyway, this is something that you have to do yourself. Researching and where to go – you can research on LinkedIn, and you can research the companies themselves, the profile, the jobs that they have, the people that work there. Research could be a lot on LinkedIn itself – it's not only the company profile. The question here is to understand how your skills match the required ones. (47:32)

Sarah: When you research the company, you research them to find out what they are looking for and if you would like to work at these companies. This is one thing. Then, before, you also did the assessment of your skills. Once you select the companies yourself, you do the matching — you see, “Okay, there are some skills that I have, or these are some transferable skills.” This is manual work, actually. [chuckles] But what you can do is strength tests, for example, to know what your skills are. It's not part of the question, but in the assessment of your profile, there are resources to see your strengths, your interests. I can share the resources later. What do you suggest, Alexey? To share them later? The resources. (47:32)

Alexey: Yeah, you can just send us links and we'll include them in the description. (49:13)

Sarah: Yeah, there is the Gallup test. There is the HIGH5 test, which is a free alternative. Then, there are also the interests. One… I don't remember the name of the platform, but there are some platforms to help you with your skills – figuring out the skills and the strengths. Researching is on you. [chuckles] (49:18)

Alexey: Yeah. Because if you outsource, then… The process of doing that also helps, right? If you just outsource and somebody comes with results, it might not be what you actually need. Right? (49:41)

Sarah: Yeah, it's like I was watching for my business – talking about the marketing. You have to do some work yourself to know the core message and so on. And then you can outsource. But there is a part that you have to do. (49:52)

How to land a part-time job when the company wants full-time

Alexey: Yeah. The next question is from Aniko, “Do you have any advice on how to tackle searching for a part-time job? Should I apply for a full-time position and say, ‘Look, I only want to work half-time.’?” What's the strategy here? I imagine a situation where a mom wants to go back to work, but she still doesn't want to go full-time. This is a scenario where this situation can appear. (50:08)

Sarah: I think it's a personal choice. But there are some strategies. The first strategy which I found more recommended is to not say what you want in the beginning. For example, you saw a job that is full-time, but you are interested in working at this company. After researching the company, you can even apply spontaneously, by the way. Once you have the list of your companies, you can apply spontaneously. So this person likes to work at this company, but either full-time or there is no position. What you do is share your interest to work at this company, and then you can either tell them from the beginning “part-time,” or go to work full-time in the beginning. (50:40)

Sarah: Then, once you prove yourself, then switch to part-time. That's the recommendation that I found when it's about full time. Because usually, when you say “part-time,” and it's full-time, they will say, “Oh, no. We are looking for a full-time position,” and it stops. So I wouldn't. If I applied for a full-time job at the first interview, I wouldn't say, “I like this job, but I want to work part-time.” At least in the advances – not from the first interview. I also know someone I helped from the beginning, she mentioned part-time. So it depends. How much is it a priority or how negotiable is it? But it's better to keep it full-time in the beginning. (50:40)

Alexey: I imagine that… If we talk about Germany – in Germany, the probation period is usually six months, so you’ll probably need to work full-time for at least six months, right? Before you can ask for a reduction in hours. (52:30)

Sarah: Yeah, I would say that during these six months that you’re working full time, if the company is flexible… Target specific companies that have the flexibility, that can give you the remote option, and then just focus on that. Then once you are either progressive in the interview, you can tell them… They are more interested in you because they think, “Okay, I want this person.” It's not like you said in the beginning. The flexibility can make full-time work easier. So it helps to experiment with that. (52:48)

Age is not a factor

Alexey: Thank you. “I worked in BI for 5 years. In the last year also with some tools like Alteryx and Tableau (a dashboard tool). Now I have time to study data engineering. I am 42 years old. Am I too old for the move?” (53:30)

Sarah: Okay, you are not too old for the move. [chuckles] (53:53)

Alexey: It doesn't matter what age you are? (53:56)

Sarah: It doesn't matter. It's true, maybe there is some discrimination based on age, but legally and so on, your age is not an issue. You don't have to put it in your resume, by the way. [chuckles] Don't put your age in your resume, because it's personal information. From there, you can focus on showing that you practiced. You say, for example, “I have practice with Tableau and some other tools. I also studied data engineering.” So focus on that in your resume, in your LinkedIn profile. Show how you can already deliver value for the companies. (53:58)

Sarah: Because what do companies care about? “Is this person going to give me the results that I'm looking forward to?” So the return on investment. “I'm going to give you a salary. What can you give in return?” So focus on that. Move away from your age, experience, and so on, and focus on, “How can I show them that I can deliver them these results?” Once you show them how you can deliver the results, they don't care about your age. You can even use it to your benefit – if you have previous experience and so on (transferable skills) that can be beneficial. This can be an advantage. (53:58)

Alexey: If somebody in the company thinks that age is important to them, then maybe… (55:32)

Sarah: Then you don’t want to work at this company. [chuckles] Exactly. (55:38)

Alexey: It’s probably all younger people who don't have family and who just live at work – and all they do is work. Right? You don’t want to work there. (55:41)

Sarah: Yeah, because… Yeah, exactly. You don't want to work there. (55:52)

Applying for jobs after finishing a course and the importance of sharing your learnings

Alexey: There is a person who is currently taking our data engineering course. The question is, “Should I start applying for data engineer roles while I'm taking the course or after? I always feel like I'm not ready yet.” (55:58)

Sarah: So, “Should I start to apply while I'm doing the data engineering course or wait?” Okay. What I would advise doing is to start to share about your learnings. Whatever you learn in the course, during the course, share about it, try to practice it – small projects, small things that you can put on your GitHub profile. From there, maybe if you start applying… You can decide to apply after. If it's a career transition and you are in the course, then I would finish the course. During the course, I would start to have my marketing strategy – the last thing. So I would start to network with the people in data engineering, I start to share my learnings, and the challenges, and so on. Then, after I finished the course, I would start applying for jobs. But I already have my network ready, my marketing strategy ready. That's the difference, compared to someone who hasn't worked on the job search at all before finishing the course. (56:18)

Sarah: By the way, I did this myself. Because I was doing that when I was in the career transition to data science – I started a project on computer vision and I was learning. [I posted] “Okay, I faced this challenge.” on my personal website. “I faced these challenges.” And I was even talking about Visual Studio’s problems and how I changed them and so on. That helped me… It first helped some other people because I helped some other people. But that article that I wrote – I couldn't imagine that it would be referred to many times, because many people were facing the same problem and I helped them solve it. That was the first thing. And the second thing, because I was sharing what I was doing and learning, and the projects that I was working on, I got an opportunity. “Come talk about your projects that you're working on.” This already showed me results when I went through my transition. (56:18)

Alexey: Yeah, thanks for confirming that we are on the right track. In our courses, we encourage students/participants to share what they learn publicly. We even give them a bit of extra motivation. We score the homework and for each post they make on social networks, when they share something, they get extra points. Now you have an answer of why you should do that. [cross-talk] (58:36)

Sarah: Don’t only set a goal like, “I have to share it to show other people.” If you frame the goal as, “Okay, let me help another person. Let me engage with people and learn from them.” Helping people who are not at that part yet, and engage with the people who are more experienced. Just make it as an experiment that you're doing with others. That's it. (59:12)

Alexey: Yeah. You'll be surprised how helpful it is, actually. (59:42)

Sarah: Yeah. [Helpful] for you and for others. Because when you try to talk about what you’ve learned on your project, you are making it easier for yourself later. At an interview, you will remember it better, because that helps you memorize. (59:46)

Alexey: So, this means that it's not just you writing a post on LinkedIn, “Hey, I learned Docker!” “Cool.” Actually write a post about what you learned, put it on some blogging platform, and then you share the post. (1:00:01)

Sarah: Or maybe some challenges at the end. You say, “Okay, but I faced this challenge.” Ask for [people’s] opinion. [cross-talk] (1:00:14)

Sarah resource recommendations

Alexey: Okay, we should be wrapping up. I see that there is a comment, “Gallup test is the best way to know the top five skills. I did it twice before.” We talked about that. You can use these sorts of tests to figure out your strengths. I think we should be wrapping up. Maybe, before we finish for the day, are there any resources, books, courses, YouTube videos, or whatever, that you can recommend for people to learn more about this topic? (1:00:26)

Sarah: Yeah. Thank you, Alexey. I just put a link for My Next Move. It’s a website where you can do the tests to see what your interests are. That's good because it helps guide you, for example, in terms of what industry you are going to be more interested in contributing to – also what roles. That is a good resource. Otherwise, I would recommend the book Feel the Fear and Do It Anyway by Susan Jeffers. Also, a book that helped me a lot is The Success Principles by Jack Canfield. Otherwise, it would be the podcasts Awesome at Your Job, DataTalks.Club, [chuckles] and Career Contessa, which is another podcast. (1:00:58)

Alexey: You said you put the link…? Did you put this to YouTube? It doesn't like when somebody shares links? So can you send it to me in chat and I’ll publish it? There’s anti-spam filters. I will now publish it – put it to the live chat so everyone can see it. I will also add this to the description. That's all we have time for today. Thanks a lot, Sarah, for joining us today, for sharing your experience with us, for sharing all these tips. We covered only two out of four pillars, but I think that's already a lot of information and it will help many, many, many people. So thanks a lot for doing that. If somebody wants to find out more about what you do, we'll share all the links in the description (1:02:02)

Sarah: Yeah. And I'm on the Slack channel. I'm there. So if you have career questions that weren't answered, you can post them again in that channel and I will reply. (1:03:00)

Alexey: I noticed that you answer questions, so thanks a lot for doing that. (1:03:11)

Sarah: You’re welcome. [chuckles] My pleasure. (1:03:17)

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