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Inclusive Data Leadership Coaching

Season 18, episode 1 of the DataTalks.Club podcast with Tereza Iofciu

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Transcript

Alexey: This week, we'll talk about data leadership coaching – inclusive data leadership coaching. We have a special guest today, Tereza. This is not the first time Tereza is here. We already interviewed her. Was it like two years ago? (1:26)

Tereza: I think so, yeah. (1:42)

Alexey: Yeah, quite some time ago. Back then, we talked about reading job descriptions. (1:43)

Tereza: Yeah. Data job descriptions. (1:50)

Alexey: It was a very insightful episode, where we talked about how you should read job descriptions and what you can do about that. Check it out. If you go to DataTalks.Club, there is a podcast section – and if you just look for Tereza… We haven't interviewed that many Terezas – only you, I think. So you'll find that. (1:52)

Tereza: We can share the link. (2:12)

Alexey: Yeah, we will definitely do that. So Tereza coaches about leading with empathy. She has helped hundreds of people to level up their data career. This is what we'll talk about today. Welcome to the show. (2:14)

Tereza: Thank you for having me. (2:31)

Alexey: The questions for today's interview were prepared by Johanna Bayer. Thanks a lot, Johanna, as always, for your help. Let's start. (2:33)

Tereza’s background

Alexey: Before we go into our main topic of data leadership coaching, let's start with your background. Can you tell us about your career journey so far? Also, maybe you can mention what changed in these two years – between our last interview and today? (2:41)

Tereza: Okay. Yeah, so my career background. I studied computer science, like many people, maybe [chuckles] I don't know. No. [chuckles] I started computer science and then I did a PhD in data science – very randomly. It wasn't called data science back then. When I did it, it was called information retrieval and natural language processing – long, long ago. Then I worked as a data scientist at XING (the German LinkedIn). And then I worked at FREE NOW as almost everything: data scientist, data engineer, product owner, senior data scientist, data science lead, senior data science… (3:00)

Alexey: Full stack data scientist. (3:42)

Tereza: Everything. And then, at the end of 2020, I switched to… I basically needed a break from being responsible for product and I thought that I prefer teaching other people to be responsible for products, so I can take a step back and then recharge. So I joined Neuefische, where I first worked as a coach – just teaching other people data science, coaching people to switch and change careers from anything they had (with or without background in data, with or without background in industry) and basically leveling up or stepping into a data career. That was like 2020 – so four years. Then I took over… Well, I built up the team. It was kind of a very ad hoc team before and then I ended up leading the team and then, at some point, I kind of stopped coaching people and just started just taking care of the team and growing the team. Then we also merged with Spiced and then the team was huge and… Yeah. So that's my background. (3:44)

Tereza: And then, last year, I studied… I noticed that there's kind of a little gap in industry – more like a mindset that was bothering me. A lot of people think, “Oh, I know exactly how to become a senior data person. I just do a lot of courses and I become a senior data person and then changing into leadership is just like, ‘get the role and then just do it.’” I mean, you also see it in senior engineering – in a lot of companies, people are promoting the next lead to be the most obvious senior in the team and then that senior is like, “I mean, I've learned stuff successfully before, how hard can just doing it and learning on the job be?” That's a lot of situations, where you think about all sorts of, let's say, not optimal leadership – People thinking that just learning on the job is enough and not taking it seriously. I don't think it's people's fault; I think it's the companies’ fault. A lot of companies are not really investing in their employees to… [cross-talk] (3:44)

Switching from an Individual Contributor to Lead

Alexey: I think I needed something like that when I got promoted from Senior Data Scientist to Lead. (6:17)

Tereza: Yeah, so… Yeah. (6:21)

Alexey: Because it’s like, “Okay, now you have these responsibilities that are completely different from what you were doing before and you're expected to do well in them. So go figure this out.” (6:24)

Tereza: Because you’ve done well before, right? I mean, how hard can it be? It's like… (6:35)

Alexey: Except now you don't do anything from what you've been doing before. The work is totally different. (6:38)

Tereza: Exactly. You were a good developer before and you could evaluate what it means to be a good developer, and now you have to work with people, and you're totally, like, “How do I even know if I'm good at this?” [chuckles] Nobody's telling us even that, you know? “What does it mean to be good at leadership?” Or “How do I do it?” So yeah, I think this is more like a general mindset going on and some people are thinking, “Okay, I should take this…” That's something I was writing at some point that, in my opinion, switching into leadership from any kind of engineering or data role is kind of like a career change. I think if we see it as a career change, then we are investing in it like, “Okay, I'm a new learner. I'm a new person. I’ll get into it.” So then I started offering coaching – more like incognito. And then this year, it's less incognito. And, and also providing… [cross-talk] (6:45)

Alexey: What do you mean by incognito? Like you didn’t officially announce on LinkedIn or anywhere? [Tereza agrees] But people are still coming to you for that. (7:43)

Tereza: Yeah. First, I did last year – I think I offered some free coaching sessions in exchange for feedback. [This was] kind of to also understand what kind of problems people have. I think I had two targets – there were the, well, data leadership people who wanted to become better leaders. Again, this is a point where I'd like to specify that, in my opinion, you don't need a leadership title to want to be a good leader. I think leadership skills can help you in your career… There comes a point in everybody's career, when you switch from “Somebody tells you everything that you have to do,” to you figuring out what you have to do. And that's like leadership skills. (7:52)

Alexey: I have a story about that. I remember when I was in the role of a Data Scientist and when I had a problem – when they approached my manager – what he told me was like, “Look, you're a lead for a reason. And this reason is you have to figure out this stuff on your own. I can help you, I'm always here to support you. But like… I need a list of ideas from you. So come up with this list, talk to people, see what they need, and then let's talk again in a week.” Something like that. (8:44)

Tereza: Yeah. I mean, the higher you get in your career, whoever your manager is will not really have so much time to have you on their mind 100%. [Alexey agrees] And I think at some point, people really high up in their career, like the VP and whatever – they don't even have one-on-ones anymore. There's nobody who knows 100% what you're working on. So in order for someone to really help you 100%, they need to be totally aware of your situation. (9:15)

Tereza: If you just have a one-on-one once a month for half an hour, and otherwise you don't really see your manager that much – that's not enough time to… If you’re going like that, just say, “Hey, I have this problem. What should I do?” You will just get an… and they go for helping you to give you a solution – that solution is most likely to be super nonoptimal. there'll be (9:15)

Alexey: Because like I have a totally different context, they're busy with other stuff, and they don't have time to really go deep into your problem and then give you advice. Right? (10:14)

Tereza: Exactly. And that can happen. I mean, it doesn't have to be that you're high up in your career. It just has to be – you can be in a company where they're bootstrapping everybody, and then you have a manager that is managing 15 people. Everybody knows about the pizza (by the way, Death by Pizza [shows T-shirt]). Everybody knows about the pizza – you should only manage a pizza that you can eat, so seven, eight slices, and everything more than seven, eight people that you are managing is supposed to be too large to do a good job – direct reports, I mean. (10:23)

Alexey: Why do you have this “Death by Pizza” T-shirt? Or is it unrelated? (11:02)

Tereza: [laughs] Totally unrelated. I'm organizing this conference in Hamburg – Python Pizza Hamburg. And then I saw this T-shirt which was at a music festival in Hamburg – Reeperbahn Festival. It's by an artist from Austria. And I thought, “Oh, I'm actually running the conference in a couple of weeks.” So I bought the T-shirt for the conference. This is usually what I wear at Python Pizza. [chuckles] (11:08)

Python Pizza and the pizza management metaphor

Alexey: Since you mentioned that, maybe you can briefly talk about Python Pizza? What is that? (11:29)

Tereza: Yeah. (11:36)

Alexey: Is there pizza? (11:38)

Tereza: Python Pizza is the best Python conference ever. (11:39)

Alexey: Because there's pizza, right? (11:42)

Tereza: There is pizza. And there is a big focus on having newcomers give a talk. All the talks are 10 minutes long. When you give a 10-minute talk, you actually force people to just talk about one idea – one topic, and it's very focused. Also, you can listen to a 10-minute talk, even if you don't like it, because you know – it's over in 10 minutes. So you don't need… [cross-talk] (11:45)

Alexey: You don’t need to sit there and scroll [through your phone]. (12:12)

Tereza: Exactly. So I think the conference just happened in February also in Prague. I think the Berlin people are going to bring it back this year to Berlin. Some Berlin scene [people] might know it. It comes from Napoli actually – of course. I mean, it does come from Italy. It’s just imported. Yes. (12:14)

Alexey: Going back to your idea of “You cannot eat too much pizza – six, seven slices at most. And the same with the team – you cannot manage more than six, seven people at once.” Right? (12:38)

Tereza: You can but… Well, I mean, you can also eat a very large pizza, right? But then you kind of get sick. (12:52)

Alexey: How do you feel after that? Because every time I eat pizza… I don't feel that it's enough for me. Right? And then I eat, eat, eat – until it's super too much. [Tereza agrees] Just too much. And then like, “Okay, no. It was too much.” (12:58)

Tereza: And that's the point. That's the same. Then the problem is that the next day, you will not be able to eat pizza. You're gonna be like, “Oh, I ate enough pizza for the whole month now.” [Alexey agrees] That's what happens when you… you can manage a lot of people. I mean, that is not impossible, but it's not sustainable. It's not sustainable for your own personal health. And it's also not gonna be really good… Just like with pizza, right? I mean, the first three slices are amazing and then the next slices start to be like, “Okay, I've already eaten.” That's why it's nice to go with friends to eat pizza so you can swap slices from their pizza – just because it gets boring, eating the same pizza. [chuckles] I don't know. It starts feeling like you cannot put as much interest and effort and work into a slice (or a person) to do a good job for that person, I think. (13:14)

Learning to figure things out on your own and how to receive feedback

Alexey: So do you think we can learn these skills? If I'm a senior data scientist and, all of a sudden, I find myself in this situation where I need to figure out what to do without being told and I'm expected to actually not be told but figure things out [on my own]. What's the most effective way of learning this? You mentioned that, “Okay, I'm an engineer. I can figure this out as I go.” Which is what most people probably do. But this is not always the best… the most optimal strategy. But is this the only strategy or can we do it better? (14:14)

Tereza: Well, the first step is to acknowledge that this is a completely new thing that you have to learn. Maybe the same mechanisms that you were using before to learn software development (or data and whatever) are not going to match 100%. So I think there's [the first] part – you need to find some courses that are really [specifically] for this – just as you read the book, did some tutorials, and so on, for other stuff at some point in your career. This is the same – be part of some conversations. Because it's important to start having awareness. At first, you're not really aware of the new scope of problems that exist. It's just like… You don't even know they exist because you've never had them before. (14:56)

Tereza: So it's not about always having the solution for everything or knowing how to deal with it, but it's also opening up to this awareness that, “Okay, this is a different kind [of problem]. This is a problem that can happen. It has happened to other people. And then when I notice it, I know how to identify that the problem is coming and what to do about it.” And I think the second thing is to work together with the team, and be transparent with the team that, “Hey, look. I'm in a new role. It's completely new. And I will need your help in order for us to succeed. In order to get your help, I don't want to make a mistake over and over again and nobody feels safe to tell me about it. Then I ended up having all bridges burned, and so on. So let's set up and build up a feedback culture in the team.” (14:56)

Tereza: I think that is the first skill to learn in leadership, in my opinion – to accept feedback… Without saying, “I think you're lying. I didn't do that.” [chuckles] (14:56)

Alexey: Sorry, I interrupted you. Did you finish your thought? (16:56)

Tereza: I think so. Yeah. (16:59)

Alexey: You mentioned courses and then I remembered that for me, I also recognize that I'm an engineer and this is a totally different thing. Now, instead of doing things myself – I don't have time to do this anymore. And I still think I am trying to… I'm saying, “Okay, I will take this task because this task is interesting. This is complex. This is exactly what I like doing.” But because I have so many other things, these tasks never end up getting finished. Then I have to ask other people to take it over. And then, I realized that there is a problem, and then I found courses. (17:02)

Alexey: For me, the courses I took were quite difficult in the way the information is presented there. Because as an engineer, I'm more used to this problem-solution approach. I have a problem, I go to Google (I just look for the solution) and then I apply the solution. And this is how my mind works most of the time. There's a problem, there’s a solution. But in the courses, they start talking about psychology, they start talking about all these things that I have no idea what they are. All of a sudden, this became important for me. (17:02)

Alexey: Instead of architectural decisions that I'm used to making as a Senior Engineer, now I have to think about other things. They just are super unusual, unnatural, unknown. I just feel completely lost when I even look at these courses. Maybe I was unlucky with the courses? Or what do you think? (17:02)

Tereza: Personally, when I stepped into leadership, I got training at work. The company FREE NOW basically hired a trainer that would train all the people that were new to leadership or stepping into leadership via a two-day workshop. I think this training… So you see, it's not a course – it's training. First, it's more going towards something that is more like training. But this training was also more like, “These are situations and these are frameworks to deal with those situations. How do you notice the situation exists? Then there’s a framework and then exercises to apply it.” So now, when you… [cross-talk] (18:52)

Alexey: [inaudible] For example, you need to give feedback because somebody… I think this is what you mentioned, right? Somebody is maybe not doing a good job, but you don't want to be super upfront and say something like, “Your work sucks.” Because this can hurt other people. You're trying to be direct – you're trying to convey as much information in as little time as possible, because this is what engineers like doing. But if you just say, “This sucks,” that can hurt other people’s feelings, right? So you need to train… You need to learn how to give feedback correctly. (19:43)

Tereza: Yeah. I always like to do the feedback training as a team training – so not just the Lead learns how to give feedback, but the whole team learns. This feedback training can be done once a year – you always get new people in the team and it's just something that takes practice. It’s just to bring people awareness that, psychologically speaking, however people give you feedback, it's going to feel uncomfortable. [chuckles] There is not much around that. The only thing that you can learn is how to react to something that is uncomfortable. That is to say, “Okay, I will be uncomfortable – I'm getting feedback now. It will be uncomfortable.” (20:18)

Tereza: It might be hard to hear, but on the other side, you also want to build relationships with your employees. That's why you want to invest in these relationships to a point where there's trust, and then you can tell somebody, “The work that you did was not good. This does not mean that you are a bad developer. It's just this particular work that you did.” I think this is something that a lot of people are struggling with – to get their self-worth from another place other than their work that they did. And then you cannot criticize the work without the person getting upset. That's something that is just something that takes a little bit of work. (20:18)

Alexey: I think this applies to every aspect of life. I remember I was reading a book about how to give… Not feedback, but how to tell your kids about certain things. One of the ideas there was – you never criticize the kid, but you criticize the actions. Like, “Look what you did. These are the consequences. It's not because you're a bad kid, it is because this is what you did.” (21:58)

Tereza: Yeah. I mean, otherwise, your kid learns that they're always bad. And then whatever they do, they anyway can’t make it better. Yes, but this is not how we are used to… At least, I don't know, maybe older people. We grew up with a little bit of a different kind of – our parents, I don't think they got feedback training in the workplace and the books were different back then. So yes, I don't think… Unless you get intentional about learning how to give feedback, it won't really come naturally. (22:26)

Tereza as a leadership coach

Alexey: What do you actually do as a coach? One thing you mentioned is that you organize these feedback sessions (team trainings) where the entire team learns how to [give feedback] (23:07)

Tereza: That’s when I was doing it as an employee. I was doing that when… [cross-talk] (23:17)

Alexey: But what do you do as a coach? (23:20)

Tereza: Well, currently I have a lot of people, I think, coming in that are more struggling with becoming impactful at work in order to get either promoted or to “feel impactful,” so to say. So I think maybe in data, this is a little bit trickier than in engineering itself. There still seem to be, I think, a little bit of unrealistic expectations of data teams. It's like – yeah, it's gonna deliver value, and it's gonna be awesome, but everybody forgets that you need to invest in a lot of foundation work because the data has to be reliable and daily and in real time, or not in real time, and so on. (23:23)

Alexey: [inaudible] [cross-talk] (24:22)

Tereza: And that's like, invisible work. (24:24)

Alexey: Yeah. So it's about setting the wrong KPIs to assess the person. You need to highlight that. Right? (24:25)

Tereza: Yeah need to… And then it feels like you're not really doing anything. [chuckles] It's very hard for to say, “Oh, I've achieved something,” or “The work that we're doing is impactful,” because you're not always delivering customer-facing products, because you're still building foundations and so on, or the velocity (the speed with which you're doing things) is not as fast as some people want. Also, how do you have this “training the leadership mindset” for work? Which is… Leadership mindset, as I said, is not necessarily that you are a Team Lead, but you are in roles that require leadership skills. Again, coming back to data, I’m not sure if you saw that there are some posts about it, but a lot of people are starting to say that “every data scientist has to have a ‘product mindset’”. (24:32)

Tereza: Like, you can't just implement your little model and not care about who's using it and what other teams are going to do with it, and so on. Everybody starts to be expected to be [engaged in] stakeholder management, and to be able to collaborate with people from different teams, and on their level, on their expectations, and so on, and then communicate in a “timely fashion,” and so on. I think this is leadership – this goes into leadership skills. Because, in the end, you need to invest a lot in communicating, you need to invest a lot into working with other people, and you need to invest in influencing other people, when you actually don't have authority. Because you won't have authority as a – let's say you're a data scientist – you can't tell the front end team what they have to do, you have to convince the front end team, let's say. I'm simplifying here. So I think this is also stuff that people are coming with, and I am trying to figure out ways to get better at this. (24:32)

Alexey: I want to summarize what you said. What you do is help people train their leadership mindset, which is about learning different leadership skills, which you probably need, even if you're a data scientist (senior data scientist, maybe not even a Team Lead yet). These are communication skills, working with other people, influencing without authority, and all these sorts of things. (26:51)

Tereza: Yeah, understanding the value – having a little bit of a strategic mindset about your work. Start to think about, “Okay, how does my work connect to the company goals?” And I'm not even talking about KPIs because KPIs are sometimes going through a lot of… I mean, some companies have very clear and well-defined KPIs, but sometimes that's not the case and you're supposed to kind of do guess work on what the goal, or the strategy, or the mission and the vision of the company is. If you want to be impactful, and successful – you want to align the work that you do (the projects that you're working on) towards that. (27:19)

Alexey: What you said, I think, when I asked you what you do – you said, “Most people who find me have these sorts of problems: how to get promoted, how to have more impact.” But I'm wondering, how do they find you, exactly? Are you on some sort of coaching platform? (28:07)

Tereza: No. (28:27)

Alexey: Do they reach out to you directly, or what? (28:28)

Tereza: I currently have a website, which is also going to be changing a bit. But basically, I'm posting on LinkedIn, and I have a Calendly link that people can book. I'm also offering – I've also offered… These are sometimes coaching for longer periods of time – people have more complex problems, but I'm also offering “one-shot” coaching (or One Shot learnings). So I'm also offering one shot coaching for whoever wants to just have a session and this time can be something like, “Hey, I have a job interview and I need to prepare for it.” Or I've had people come with, “I need to switch from one country to another. How do I relocate and adapt what I know about my career in this country to this other country, so that I can get a job in this other country?” So it can also just be something that we are discovering and it's in one hour, where we have a little bit of an in-depth conversation on that. Or CV review. That can also just be a one-hour session. (28:30)

Alexey: So if I correctly understood what you said – you regularly post on LinkedIn and then in your profile, there is a link to your Calendly and people find this link. You probably say that “I coach” or “I consult,” [Tereza agrees] and then people find the link to your Calendly, where they can book a slot with you and discuss all these problems. [Tereza agrees] And then what you offer is a “one-off” consultation, which is just one hour, or more regular coaching. [Tereza agrees] Okay. And how did you actually find out that this is the way you want to do it? Why a Calendly link? Why LinkedIn? Why not, I don't know, partner with a website that offers coaching? I know there are websites where you can find some sort of catalog (a marketplace) of mentors, where you click “Okay, I need a data leadership mentor.” (29:51)

Tereza: So most of the websites – there are two types of websites: the ones where there's payment, and then there's a lot of them that are for free. [chuckles] Which is nice, but I think… The only place I'm offering free coaching at the moment is on Coffee Code Breaks, which is a community for women who are needing mentoring. There I offer a session per month for free. But otherwise, yes, I am… Why Calendly? I mean, if you have to manage more than two or three calendars, I think that's kind of the only way to make [it work]. For me, if it's not in my calendar, then the thing is not happening. (30:54)

Alexey: Basically, it’s trial and error, right? You found out, eventually, that this combination works for you in the best way? (31:36)

Tereza: Yeah. So basically, there's some people who are posting on, “What is a strategy? What are the tools that you need?” And there's different levels of tooling and then there’s the low payment entry, like when you don't pay so many things for so many things. The “starter pack” of coaching would be Calendly – maybe Zoom or another platform or Google Meet still for free, though, it's kind of annoying. [chuckles] Anyways, then you have how much money you want to invest in it. It can get more fancy – more CRM-ish. (31:43)

Podcats

Alexey: Is it the only thing you're doing right now? Or do you have other activities? (32:26)

Tereza: Well, I'm also doing the PyPodcats. (32:31)

Alexey: Pod cats? (32:34)

Tereza: Podcats. (32:36)

Alexey: [chuckles] What is that? (32:37)

Tereza: It's a podcast that we started. It's from the Python Software Foundation, and we want to acknowledge the contribution of women in the Python community – women and people from underrepresented groups. We've started with… We now have two episodes out – one episode goes out per month. We are four in this: Mariata, Georgi, Cheuck, and me. We do two hosts, one guest per podcast. So that's something fun that I'm doing. Otherwise, I'm also partnering with Shades and Contrast with Zamina, where we will offer more trainings and consultancy gigs, I guess. (32:39)

Alexey: Shades and Contrast? [Tereza agrees] What is that? (33:30)

Tereza: It's a company that Zamina has launched and she's doing. She also wants to go for Responsible AI – bring more Responsible AI to the German market, so to say. So it's training and consultancy. (33:37)

Alexey: There is consulting and training. (34:00)

Tereza: And training, yeah. But yeah, sometimes you go to a company, you help them set something up, but then you also want to leave. And for that, you want to help the team be able to work with whatever you help them set up and then they might need training. (34:04)

Alexey: So for you, your main activity with which you earn a living right now is coaching. (34:21)

Tereza: Yeah? I mean, I'm in transition. For me, this year is the year of trying to do that. Yes. (34:29)

Tereza’s coaching framework (selling yourself vs bragging)

Alexey: Okay. This is very interesting and motivating, so thanks for sharing that. [Tereza agrees] How do you usually…? When somebody approaches you, you probably have some sort of framework of how exactly you're going to help them. Do you have something like that? How does it usually go? (34:38)

Tereza: I first obviously ask about their background, and why they are there – what do they expect to get out of it? I think the hardest struggle is just to coach people? You know, you have training and coaching, and a lot of people, I think, come to me because I have a data background. Then, a lot of people expect training as well. Because when you do coaching, theoretically, you're never supposed to give advice. That's like the extreme of coaching. I would just ask you open questions until, by the end of the hour, you come up with an “aha!” moment yourself. I think I kind of go a little bit in the middle, because there's also sometimes advice like, “There's a book. There's a course. Have you tried this?” That's for the people coming from the data background. (35:00)

Alexey: So just a general consultation, right? You can do a bit of coaching, a bit of mentoring. (36:09)

Tereza: Yeah. And sometimes you have CV reviews – sometimes, even if you're not looking for a new job, I think it's a very healthy thing to do every now and then because I think most people living in Germany… If you compare a CV from someone living in Germany with someone living in the United States, you see the difference. [chuckles] There's so much ownership in, “I did it! I achieved that!” in an American CV, while in Germany, it’s like, “Yeah, we did this together, and we built it.” It's very, let's say, unassuming. We are also really bad and a lot of us feel very uncomfortable talking about our achievements, because they're like, “Oh, but that's bragging, right?” I mean… Is it? [chuckles] I don't think it's always bragging. (36:14)

Alexey: But you need to be able to sell yourself, right? And Americans… [cross-talk] (37:13)

Tereza: Yeah, but also to yourself – you need to be able to “brag” to yourself. I'm talking about – even to ourselves, you're like “I really have achieved that and I will own it. And I will feel much more confident from just acknowledging that I did that.” (37:16)

Alexey: So what you're saying is, we shouldn't feel that this is bragging – this is more like acknowledging that we have done this. It shouldn't feel like bragging because this is a fact, “I have accomplished that.” (37:30)

Tereza: Yeah. Like you in the podcast, right? I mean, in the whole DataTalks.Club – the whole community and the courses and everything. That's quite a big achievement as a side gig (unpaid). So I don't know how often you brag about it. [chuckles] (37:41)

Alexey: Well, right now it's my main activity. Bragging? Well, when people ask me what I do, I just tell them. Does it count as bragging? [chuckles] Maybe? (38:06)

Tereza: See. It's already like this, “I'm being put on the spot.” (38:24)

Alexey: I think you did this on purpose. [chuckles] (38:32)

The importance of retrospectives

Tereza: I did it on purpose. [chuckles] I think there are people that are finding this way more… They feel way more super confident and are like, “Oh, yeah. I totally did that.” And it's not like they're born like that, you know? It's just a matter of practice. You look in the mirror 100 times a day, and you tell yourself (okay, maybe five times a day) and you tell yourself, “That's what I did.” I think, also, we don't really do retrospectives with ourselves that often. I mean, I've seen a lot of people on LinkedIn – they did that at the end of the year. In January everybody was doing retrospectives and their New Year's resolutions. I decided to do my retrospectives at a random date this year because… Why New Year's? [chuckles] (38:33)

Alexey: Is this something you also recommend your clients to do – the retrospective? (39:29)

Tereza: I think that reflecting… Sometimes you may need another person to do it, just to ask the right questions, but yes – reflecting on everything that you’ve learned or achieved in, let's say, the past year, or in a past job… Starting to put things in writing on a piece of paper – it can definitely help you get a sense of achievement. Sometimes when you feel… Especially if you're in a workplace where, let's say, you don't have given goals and so on from your work. You work for 4 years in the same job – no change in title and so on. At some point, it gets… Actually, the best example is – ask someone to give a talk at a conference and when they reply with, “But what am I supposed to talk about? Nothing that I do is really conference-level.” If that's your answer, then you should definitely retrospect. (39:36)

Tereza: I did this course on writing online, Ship 30, and the idea there was like, “What do you write about? How do you come up with ideas?” There was the concept of the “two-year rule”. And the two year rule is to write down everything you've learned in the past two years and to understand, to reflect, to acknowledge the fact that there are people that are two years behind you. Then, when you want to talk or present or say what you have achieved, it's like you’re talking to those people. So you always have an audience. That's also at a conference, you are likely to have people that would be two years behind or one year behind. (39:36)

Alexey: That's such a good piece of advice. I regularly find myself in this situation, when somebody approaches me and says, “Hey, we want to invite you to talk at a data conference.” And then like, “Okay, I've stopped being an individual contributor like four years ago and all I did was talk to people as a data science lead. What am I supposed to talk about?” (41:35)

Tereza: I know, but that's the curse of switching from an individual contributor to leadership – you get a huge… This is what I was also saying, your goals are not well-defined in your work. Before, you knew exactly what your value was, and now you don't. Then it becomes something where you don't talk about it even to yourself. You're trying to [say something] like, “Let's not acknowledge the elephant in the room – the fact that I have no idea what I'm doing, and I have no idea how to talk about whether it has any value to someone. Sure, I lead 15 people. Nobody quit while I was there. But doesn't that happen to everybody?” It's like, “No, it doesn't.” And “I got good reviews as a manager. Doesn't that happen to everybody?” “No, it doesn't.” “The people under me grew in the role. Within two years, five people leveled up. Doesn't that happen to everyone?” “No, it doesn't.” (42:01)

Alexey: That’s a lot of conference-worthy material. (43:13)

Tereza: But then you have different conferences now – you have to go to different conferences. (43:16)

Alexey: Yeah, so it wouldn’t be a PyData conference where you talk about putting your model for fraud detection in production, but it would maybe be some team lead conference. (43:22)

The importance of communication and active listening

Tereza: For example. But you could also talk about that at PyData because like, “Let's put the model in production.” But what happens behind the scenes of putting the model in production? You have to convince the whole company that this model should be in production. You have to convince your team that they have to deliver it – by “convince” I mean, agree and work towards the goal. You're not doing it yourself – alone. So all of this stuff, without this kind of work, there would be no model to go into production. There used to be these conversations before, everybody used to say, “From data science, only like 10% of the models make it to production.” Or I don't know – some random percent that was very small. (43:38)

Tereza: And that was a problem, because like behind every technical model – every technical piece of code that you're writing in a data team – you have way more work in terms of communicating about it. It's the same with open source. I'm part of the Python Code of Conduct workgroup and now I noticed, as part of this group, how much communication happens behind every issue and pull requests and so on. And that's where the art of communication [comes in], and I think it's like… [cross-talk] (43:38)

Alexey: The community is super distributed – people are everywhere in the world – yet, they need to make a decision. The decision-makers are spread around the globe, but you need to reach some sort of consensus – like, “Do you accept the pull request?” Or what needs to be done. (45:10)

Tereza: That's why I think there's also this “volunteer burnout” that people are saying [they have] for open source, because it's not just writing code, and you're spending so much energy on talking about writing code. So I think the next time you get invited to give a talk, you say yes, and then figure out what you're going to talk about. (45:29)

Convincing people you don’t have power over

Alexey: I was wondering why there are no questions in Slido, but I noticed that I forgot to post the link. But I see that there are some questions already in the live chat. We talked about different leadership skills and one of them was leading without authority, which is especially relevant for data people – because they don't have influence over the engineers who are supposed to implement something or maybe over product people. (46:00)

Alexey: Often, the case is (at least in the company where I worked) that we all have different reporting lines. I have my Head of Data, for the Product Manager, they have a Head of Product, for Engineers, they have an Engineering Manager. In such a setup, I need to go to the Product Manager and convince them that “Look, we need to try to get this on the roadmap, because this is important.” There is no way I can go through my manager – it’s super complex because it will never reach [production]. So I need to convince that person in front of me that this is a good idea. There is a question from Antonis, “Do you have any tips regarding influencing? How do you actually do this when you don't have any authority?” Which is especially true in the case of data folks. (46:00)

Tereza: Yeah. I think one of the cornerstones is learning how to speak different work languages. How you talk to other data scientists is going to be different from how you talk to product people, and how you talk to engineers in another team and how you talk to sales and how you talk to the C-level and so on. And then it's also about listening. So practicing talking different languages, and also practicing active listening. With active listening, you start hearing what people actually have as a problem, or what they care about, what their own goals are, and what are their own perspectives. Then you can organize your message to fit that. You want to be able to communicate to the other person to the level that they will understand what you're saying. A lot of the time, our problems in communications are that we think communication is just about delivering a message. But actually, communication is about making sure that the other person understood the message the way we intended them to understand it. So that's a little bit more work. (47:29)

Alexey: How do I get them to do what I need? [chuckles] That sounds horrible. But I mean, I have a project and I need to get this on the roadmap – how do I do that? (48:59)

Tereza: Well, you just have to make sure that they will find whatever you need to be important. (49:10)

Alexey: And I do that by active listening? (49:17)

Tereza: Well, you do that by communicating why that is important, how it is important for them. You need to find out what is important for them, because otherwise you can’t link it to something that is important for them. Why should they care? If you don't connect it to why they should care about it… And, of course, you can get a lot of people to do whatever you want if you're best friends with them, which also requires a lot of investment to get to that level. [chuckles] So network – sort of networking, (49:20)

Alexey: I’ll try to summarize what you said. First of all, you need to understand what they care about, so then they also… (50:00)

Tereza: That’s the active listening part. (50:07)

Building relationships and empathy

Alexey: Active listening and then also learning the language they use. And you also need to be friends. You have to have a relationship. (50:09)

Tereza: Yeah. There was another part. This was a problem I noticed with data science people – if you were on a direct track – a very linear career path – then you most likely weren’t really exposed to how other people in other teams work and what other roles there are in a company to make things work. It was not just data science, but also software engineers, and so on. I also worked in teams where we saw that there is no value behind having a project manager – I mean, we can also decide what we have to do – there is no value behind having a product owner. While holding this view on the world, you are not going to be able to collaborate well with the product owner and a project manager. The best way to work on this view is to find ways to expose yourself – to like to put yourself in their shoes. (50:23)

Tereza: To understand, “How do they work? When is it good for them? When is it bad for them? When is it a risk or not? How do they evaluate something as good or bad? How would things be in an ideal world?” A simple example is this relationship when a product owner or a product manager goes to the data scientist and asks, “How is the project going?” After like one week. And you're like, “What? I need three two, three weeks to come up with [something]? Why are you bothering me now?” And you're like, “Yeah, but I need this because your outcome is used by another team, so I need to link this and I need to know it.” (50:23)

Alexey: What Antonis wrote is, “So this is also about empathy, then.” Which I think is right. (52:22)

Tereza: You can’t really get away from empathy. There are all sorts of ways to improve your empathy. And I think there are courses for that. I think the science behind empathy is that people who are working, that have emotional intelligence, are more successful in their career. So that's a good reason to get good at it. (52:28)

Alexey: What you mentioned is – the key idea how you can do this – you mentioned, “Try putting yourself in the other person's shoes.” If you try to put yourself in the position of your product manager, then you can try to understand what kind of problems they have to deal withю (52:58)

Tereza: Exactly, what problems they have and what they find annoying. [chuckles] Because maybe you’re just often telling them, “Hey, can you do this? Can you do that?” You don't even know how annoying it is for the person and they're polite, and then they don't say anything. But if a person is annoyed, then they're not really likely to help you out. (53:17)

Alexey: [chuckles] There is a funny comment, “Tereza’s description of a data scientist leader makes me think that this person is similar to a therapist.” (53:41)

Tereza: Oh, yeah. Especially if you're in a cross-functional team setup. [chuckles] (53:50)

Alexey: Okay, so this is an accurate description. [chuckles] (53:56)

Tereza: [chuckles] Yeah. When companies are saying, “Okay, we’re going completely cross-functional. And now, because every data scientist is in a different team, the data science lead does not actually hold any more responsibility for any kind of product pillar – just for the people.” That is being a therapist, yeah. (53:58)

Inclusive leadership

Alexey: I noticed that we need to finish quite soon. But there is one thing I wanted to ask you. The topic of today's interview is “inclusive data leadership coaching”. We talked about data leadership, we talked about coaching, but we did not talk about “inclusive”. So what does it mean, and how do you do this? (54:24)

Tereza: If you think about the opposite of “inclusive” being “exclusive” – you don't really get too far by being exclusive in the workplace. (54:51)

Alexey: What does it mean to be exclusive? (55:05)

Tereza: You don't want to work with other people, you don't want to collaborate with other people, you don't need other people, you don't care about other people, you don't care about their needs, you don't care about how they want to be treated. That is being exclusive. Being authoritarian in leadership – that is also exclusive. “You get your work done because I said so. I don't care if you care about it or not.” I don't see anything positive out of being really exclusive at work, and then being inclusive comes in. I mean, you have diversity and inclusion. That's also inclusion there. But I think it's also about figuring it out. It's not an afternoon of reading online about how to do it – it is figuring out how other people want to be treated and working with that. (55:09)

Alexey: So I was not actually correct when I said that we did not talk about this because we actually did. (56:10)

Tereza: Yeah, we talked about it. Yes. (56:15)

Alexey: Okay. All this empathy, and trying to put yourself in the shoes of the other person, and then understanding why they should care about our problem – of what they care about in general – these are all aspects of being inclusive, right? (56:15)

Tereza: Yeah. And then on the other side, you also have, of course, cultural and gender and team diversity. There, we all know that in product, and if we work on the data science part, we are probably in product or in analytics – it's important to have diverse perspectives when you're building something. Otherwise, you might not have a big overlap with the people you're building for. (56:35)

Alexey: I think with that we can conclude our interview. I don't see any other questions. I don't think we've covered all the questions that we prepared, but kind of anticipated that. But we touched on most of the points. So yeah, thanks a lot, Tereza, for joining us again. And thanks, everyone, for joining us – being active, asking questions. I guess that's it. (57:05)

Tereza: Yeah. Thank you, Alexey. If anybody wants to reach out on LinkedIn or connect and whatnot, do so. (57:35)

Alexey: And there is also a link to your Calendly in your… (57:42)

Tereza: Yes, I will update it now. [chuckles] (57:46)

Alexey: Okay. If anyone needs a coaching session from Tereza, you will find the link on your LinkedIn profile. Right? [Tereza agrees] Okay. So, thanks again, and goodbye. (57:48)

Tereza: Have a nice week. (58:01)

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