Feb 14, 2021 | E040 From PhD in Physics to Machine Learning Engineer at Facebook. Facebook Jobs for PhDs

Dr. Aleksandra Drozd is a Machine Learning Engineer at Facebook. After her Master’s studies in Physics at the University of Warsaw, she joined International PhD Studies. During her studies, she was a visiting researcher at University of California Davis and University of California Riverside. After her PhD, she joined Theoretical Particle Physics & Cosmology group at King’s College London as a Postdoc. In 2015 she left Physics for Data Science. In the last 5 years, she worked on fraud detection, sports trading, energy trading, and retail analytics.

Aleksandra’s LinkedIn profile: www.linkedin.com/in/ola-drozd-10782790/
Aleksandra’s Facebook profile: www.facebook.com/ola.a.drozd

The episode was recorded on February 13th, 2021. This material represents the speaker’s personal views and not the views of their current or former employer(s).

Aleksandra Drozd - Machine Learning Engineer at Facebook

Transcript

Introduction

Natalia 00:12

Just before we start, just a little disclaimer. Since my today’s guest, Ola, works for Facebook. I’d like to mention that this channel is apolitical, we only talk about careers and career development. So, how it feels to work for Facebook? Whether or not it’s a good idea to apply to work there and how to get through the interview?

So, these are the topics that interests us, and not necessarily political situation, and Facebook’s involvement or lack thereof. So, if you guys are looking for this type of content, unfortunately, you won’t find it here. Anyways, I hope you’ll have a nice time and enjoy the episode.

Hello, everyone. This is yet another episode of Career Talks by “Welcome Solutions.” And in these meetings, we’ll talk with professionals who have interesting career paths and who are willing to share their life hacks with us. And today, I have a great pleasure to introduce a friend from undergrad studies to you.

And so, when I was an undergrad student at the University of Warsaw, I was not the very best student, I have to say. I was partying a lot, but some of my peers from studies actually were hardworking students and went really far in their lives already, and are quite successful to date. One of these people is Ola Drozd, Aleksandra Drozd, who is a Machine Learning Engineer at Facebook today.

After her master’s studies in Physics at the University of Warsaw, she joined International PhD Studies. And during her studies, she was a visiting researcher at the University of California, Davis and University of California, Riverside. After her PhD, she joined Theoretical Particle Physics and Cosmology group at King’s College London as a Postdoc.

And in 2015, she left Physics for Data Science. In the last five years, she worked on fraud detection, sports trading, energy trading and retail analytics. Great to have you, Ola. I’m so happy to see you after so many years. And I’m happy to see how your career is progressing. And I’m very curious to hear your story from your own perspective.


Aleksandra’s Character Arc: From Studies in Physics to Machine Learning Engineer at Facebook

Aleksandra 02:32

Thank you so much, Natalia. Thank you so much for inviting me and for a lovely introduction. When I was preparing for the interview, I realized I was preparing an introduction. And I realized introducing yourself is actually quite hard, a quite a hard thing to do. However, when I was an academic it was much more straightforward.

You know, I just used to tell people that I’m a physicist. And it was just a very clear and concise way to tell to whomever you know; we’re speaking of who I am. My job was to me more than just a job. It was a part of my identity. And when I left academia, it was quite hard to find an equivalently easy way to introduce yourself.

So, I actually came up with a saying that I’m a failed physicist. And yes, so the short summary of my career path would be that I’m a failed physicist. I did my PhD and my Master’s in Physics. It was particularly in particle Physics phenomenology. I was researching beyond Standard Model Physics and Dark Matter candidates.

I went for my postdoc to London, to King’s College London. And some time after the next six months into my postdoc, I started to prepare to move out of academia. And a couple months later, I landed a job in Data Science. I realized that the skillset of a Data Scientist allows you to explore different industries.

So, it’s very applicable to a variety of different types of problems. So, I got to work a little bit on sports trading, on fraud detection and then football modeling, even though I don’t really know that much about football itself. But apparently, it’s enough to be good at math. To do that, I’ve worked in energy trading as well and retail analytics.

I even had a short adventure with chatbot AI. So, all those experiences within like a span of a couple years, allowed me to move into the role of Machine Learning Engineer at Facebook.

On Chasing After Purpose

Natalia 05:38

Fantastic. Actually, I am tempted to ask you some questions that just came to my mind right now. Since you know in academia, we usually have a purpose that is, you know, oriented at solving a particular problem or a class of problems. So, once you move to Data Science and you went through such a variety of projects, how did you repurpose yourself?

So, how do you feeling about your sense of purpose today? And is it related with particular problems you’re willing to solve? Or is it related more with the desire of becoming better and better at Data Science? Like how do you think about your professional mission today?

Aleksandra 06:25

That’s a really good question. I think that, I easily get excited about things. So, that’s something that I would say is in my character. And it was reasonably easy for me to move a company and then get excited about a new idea or a new project that I, you know, wanted explore and solve. The skillset for a Data Scientist, I would say it’s having a good grounds that allows you to solve problems across different industries. I think that’s the easiest way to say it.

Natalia 07:09

That’s actually a very good point. And I think there is a misconception about passion often. Because to most people, passion comes with some level of proficiency. So, once you become an expert, then you feel passionate about what you’re doing, it’s not the other way around. So, it’s often the case that people feel passionate about certain class of problems, but they don’t have any skills or any tools to actually contribute.

And then, this passion fizzles out very quickly. You have to actually feel good at something to get that job satisfaction in the end as a contributor. That’s one of the reasons why I didn’t go to Data Science myself, because for many scientists, Data Science and data analysis and statistics and programming are only as a means to reach some goal.

And it was the same for me. I learned all these skills because I wanted to answer research problems. And I enjoyed wrapping up manuscripts and teaching students but I didn’t really enjoy the bear, you know, activity of programming. 

I knew that Data Science jobs are well paid these days, there’s plenty of them, there are opportunities. So, I would have very good benefits if I went that direction. But I decided to go another direction, which is much harder usually. But I knew that, for me personally, it would not be the right way of contributing. And actually, I have a question for you about the job market in Data Science today, because the whole field was developing very rapidly in the recent years.

Is There Still Space for More Data Scientists?

Natalia 08:45

And together with the development of new Data Science, like pipelines and approaches and machine learning and AI, there was also rapid growth in employees. So, inflow of people who are interested and who are requalifying not only are the universities, but also online, by taking online courses and joining online Academy. So, there is a rapid growth of both projects, but also people. So, do you think that, now, in 2021 Data Science is still a hot field that is competitive to attract new people?

Aleksandra 09:35

I certainly think so. Data Science is still growing and I think it’s going to continue to grow. Of course, you have to keep in mind that we’re here in February 2021. So, still in the middle of the global pandemic and a very severe economic crisis. However, taking all that into account, the IT sector meaning, for example, all the big tech companies, the internet retail sector is growing.

Aleksandra 10:25

And there is a lot of need for data scientists to join the rapidly growing companies and support them with first valuable insights about their products and driving the direction of the growth. And the second one is just particularly, building machine learning-based products.

Natalia 10:54

If you could compare between the two, do you have some advice for how to tell if you actually fit one or the other better?

Aleksandra 11:04

So, the best thing probably is to try both. And I think that’s kind of what I did, I tried both. I’ve been in a position of a data scientist, that was more on the Driving Insights Team. Where the outcome of your work is not necessarily a working product, but more of a presentation or a prototype.

And based on this, your analysis or your experiments, a leadership team can make decisions. So, I found it though a little bit less rewarding personally, than building products myself. So, I tried to divert my career more into the engineering path. And I think, for me, it works better.

Natalia 12:21

Well, I think this path can resonate with many researchers who leave academia because they don’t see the beneficiaries of their work. So, I personally, I was tired with producing manuscripts that are just reporting some problems. And just hoping that someone else will read them and just possibly produce something based on my contribution.

But I would rather actually build a new feature or a new product or a new, any other output that I could see someone using. So, I feel if you’re watching us and you’re one of these researchers who have that frustration, then possibly, this is the direction to go.

Aleksandra 13:07

If I can only add one thing to this. I wouldn’t necessarily disqualify it. Giving insights on something less rewarding on its own. It is definitely very dependent on how the company or how your team is structured. It can definitely be incredibly rewarding. If you work closely with a team and you see your research or your experiment outcomes actually influence someone else’s decisions. So, depending on how the company is organized, it can be also a great experience.

On Aleksandra’s Other Experiences in Industry

Natalia 13:54

Right. And can you tell us a little bit about these? Before we move to talking about Facebook, you’ve worked for quite a few companies before. So, is there any experience that you could share with us? Like something that stuck to your mind that is a particularly nice experience that you had from all these years that you were working for these companies. Some project that you remembered as a particularly nice one or some company that you remember as like really a good working environment?

Aleksandra 14:30

Yeah, sure. The first company that I joined after I finished my Postdoc was a FinTech, a small FinTech startup, at the time called Smarkets. It had only I think, 26 or 27 employees around the time where I joined. And it was a fascinating experience. It was a quickly growing company because it was a FinTech startup.

The learning curve for myself, as someone that just comes out of academia and wants to learn how to, you know, how to be a Data Scientist, how to be an engineer, how a company works, it was an incredible experience. I was surrounded by smart and very talented people. And I also got to work on various parts of building a new piece of technology process.

Because the company was growing dynamically. because there weren’t enough hands on deck, you get to participate in building a new piece of technology in different stages. So, you could work on infrastructure. I worked on building a micro service, not only on machine learning modeling but I also had a chance to work on that. A little bit of a front-end work as well.

So like a full picture, and being a full stack engineer, I think generally joining a tech startup, it might be a fantastic learning experience. And particularly early in your career, it definitely helped me grow and accelerated how quickly I learned things.

Is There Pressure To Change Jobs in IT?

Natalia 16:42

Well, that’s great to hear that you had a good experience. So, you have quite a few different types of positions in your portfolio so far. Was it because you felt, like personally you felt the need to change and to grow? Or is it also that today in Data Science, it’s very competitive and there is some form of pressure to also build this portfolio?

Aleksandra 17:17

I think there is a pressure to build a portfolio. I think that is a very true thing to say and I think I had this pressure. And I think that particularly, like young people in their 20s, they tend not to stay, maybe. Or stay not as long in one place and move to collect new experience.

Maybe in Data Science or in engineering, in software engineering. This happens faster or within a shorter time periods than in other industries. I think there is some truth to that. Personally, for me, that was one of the main motivations why I moved and changed the company to learn something new, to understand how other companies work as well.

I also wasn’t that clear about what type of problems I wanted to work on. And working on different types of problems, definitely, helped me choose and help me understand what I want to do. So, that was to me personally very valuable.

How To Transition from Theoretical Physics to Machine Learning Engineering?

Natalia 18:46

But let me now ask you a question from the audience. Pablo asks, “What is the best way to prepare for a transition from Theoretical Physics to Machine Learning Engineering? What was the most difficult part of it? And also, did you try to also work on research in machine learning?”

Aleksandra 19:06

I think the most difficult part for me was to make the choice and decision to leave academia. As I mentioned, being a physicist was a significant part of my identity. And it was difficult to let go of it and to change your mindset and to be open to new options and new experiences. So definitely, that was really hard.

Aleksandra 19:28

I haven’t done any particular research in machine learning as a physicist. What I have started to do though, after I realized I wanted to make this move and I chose that, Data Science and AI is the direction I want to go to. I learned programming, I picked up Python and start to prepare for Python, like for coding interviews.

Aleksandra 20:00

But also, there’s an enormous amount of resources out there of how to learn Data Science. The amount of those resources, I say, at this stage is really overwhelming, if you go to Coursera or to TEDx. It’s even hard to choose any of, ‘Where should I start and what should I do first?’, go with whichever course you think is fine and just start learning.

But on top of that, I think it’s very important to find yourself a project that you feel that you care about, some problem that you want to solve and work on that for some time. It does not only give you something to put on GitHub that you can show to your future employers. But it also can reinstate in you this feeling that, you know, that Data Science is really what I want to do, or the other way. It can make you realize that you don’t actually want to pursue this as a job. So, having this experience of working on the project and accomplishing something. Just like, personally, just you and your computer, I think it’s going to be very valuable.

Why Facebook?

Natalia 21:29

Okay. So, now I’d like to ask you a few questions about, about Facebook, about where you’re working right now. And Facebook is a legendary company for many reasons. And I’m curious, what was your motivation to apply to Facebook? Why Facebook? Why not Apple?

Aleksandra 21:50

I think the specificity of Facebook, the fact that, that this is a social network and the potential types of problems that I could work on had been definitely very appealing. On top of that, I really wanted to work in one of the big tech companies, because of how amazing things I heard about it.

Particularly, from a perspective of a Machine Learning Engineer, a Data Scientist. The environment that Facebook creates and other companies, I think, from the big tech creates around working in machine learning and using machine learning in, you know, in their systems. They do it on a much larger scale than any other companies I could think of. So, I wanted to be a part of that.

Natalia 23:04

And what is your favorite aspect of working there? Is it people? Is it your particular project? Is it the fact that the whole company is so influential? Or are the benefits? Like, what is your favorite thing about Facebook?

Aleksandra 23:17

I definitely have to start with people. So, I have a fantastic, extraordinary team of super smart and super talented people. That definitely gives you an imposter syndrome. However, the situation is also weird. I joined Facebook in April 2020, which was the – during the unprecedented wave of the COVID-19 pandemic.

And I think it strongly influenced my decision on which team to join and where to focus on. So, I joined a health integrity team that is currently focused on tackling COVID-related misinformation and other health related misinformation. And I think that working on this topic is definitely incredibly rewarding and motivating. So, that’s another reason I’m very happy.

How Does Working For Facebook Look?

Natalia 24:18

Okay, that’s very interesting. I wasn’t aware that Facebook is involved in this type of topics. And can you tell us a little bit more about your daily life? I know that it’s not, you know, we are living in weird times. And it’s not a normal working scheme, probably. But like, can you tell us a little bit more about how your week looks like in the company?

Aleksandra 24:46

Definitely. So, I’m working from home and I’ve been working from home since I joined the company in April. And I can only properly tell you how things look like from my own perspective of a Machine Learning Engineer. Which I think there’s a wide variety of different roles and different teams in Facebook.

And generally, the experience of the company I think is very local in a given the scale of a large company. So, for me, I, as a Machine Learning Engineer at Facebook you’re definitely an engineer first. So, your day-to-day work requires a lot of coding and writing a lot of code. There is an incredible set of internal tools within Facebook, that allows you to apply machine learning with ease.

So, it’s really a pleasure to run experiments and to work with data on a daily basis. Which isn’t for anyone who has experience with working with data, on working in genuine Data Science. It’s not an easy thing. It takes you, often, a significant amount of time to build your experiments setup or to build your own tools.

Particularly, for example, with my experience in a smaller company; more startup when you had to build everything by yourself from scratch. So, the culture as well, is absolutely amazing. It’s like very open and I’ve mentioned all the talented people already, that you get to work with.

And even though you work from home, I think you get quite a good experience in terms of, of the social life or a good glimpse on the company culture from you know from your home office. And I really can’t wait to be able to go back to the office and experience, you know, the full of it; how it will be.

What Does Glassdoor Say About Facebook And Is That True?

Natalia 27:32

I looked into Glassdoor reviews of Facebook in the past few days and I have to say that they are overwhelmingly positive. And actually, made notes from those reviews, there are thousands and thousands of 5-star reviews for Facebook. So, I was curious what are the common points that most reviewers make and I noticed 5 main things.

So, one of them is stimulating environment, lots of room for growth, lots of freedom and exciting projects. That’s the thing, number 1. Number 2, excellent compensation and that rewards strong performance and good food apparently. And the number 3 is transparency, as you’ve said, and clear internal review process.

And also, some people say there’s less unnecessary internal processes than in other companies like Google and Microsoft and also intelligent people. And lastly there is a strong focus put on diversity and inclusion. So, my question would be, what do they add to the food so that these reviews are so positive?

Aleksandra 28:45

Oh, I wish I knew. I can’t, really, wait to try the food. Maybe if I tried, I would be able to give you some more insights on that. I think I can sign myself, my name, under all this of this positive points. There is a lot of diversity and inclusion in Facebook, there is. The culture is amazing. I think it’s the most diverse company I worked in.

And I know that, particularly, if you are in an “Underrepresented in Tech” background, you should definitely apply to Facebook, you’ll feel comfortable and well there. When it comes to other points, I think transparency is a very big one for me. And it does start very early. So, it starts with the interview process.

I remember when I was interviewing. So, Facebook has their internal recruiters and they understand the process, of course, very well. And they guide you. They are very transparent about how the process will look like from the beginning. So, you get a clear, very clear expectations as well of, you know, how you should perform and what type of interviews you are going to see.

And they also help you prepare for those interviews. So, for example, there is this seminar that Facebook organizes, that is particularly aimed at the coding interviews. I think it’s on a monthly basis or similar. I definitely encourage you to sign up, I did that before my interview. And it helps you to understand how, you know, how to prepare yourself for the interview, where to find resources etc. And then, what are the good practices? What you should be mindful of during your coding interview, to just perform at your best?

How Do Coding Interviews for Facebook Look?

Natalia 31:14

And I’m actually curious, what is the most important aspect of coding when it comes to interviews for Facebook? Because, you know, it’s a different skill to actually have that conceptual, like aptitude that you can construct a code that is efficient; efficiently solves a problem. And it’s a different thing to write it in an understandable way and write a clean code.

So like, for me, for instance, it was actually easier to come up with efficient algorithm than to actually put it in, you know, in the code in a way that other people can understand me. So, I would probably not pass any interviews to one of these big tech companies. Can you tell us, like, very briefly, what the philosophy is and what is actually the preferred? Like, what are the most important skills in coding that are, like that matter for Facebook?

Aleksandra 32:15

So I can say, from my own perspective, what are the most important things that I think matter, and the first thing is practice. So, a lot of practice in solving coding problems. So that, just as you say Natalia, to be able to quickly turn your solution that you have in mind into clear and readable code.

And you can do that in any language, you’re comfortable with. Just whatever you feel, you know the best and you’re most comfortable with and a lot of practice. And the other one is to be able to talk through your solution, so clearly. Or to talk through your thinking process about what you’re thinking and communicate on the thought process with your interviewer. I think those are very, very big points.

Natalia 33:26

Very interesting. And I really like the fact that the company actually helps the candidates in preparing for the job interviews. And I feel like in, you know, many companies, the recruitment processes are indeed very non-transparent and expectations are not clear. Whereas I think that once they become clear, it’s actually a win-win because since you know how to prepare for the interview.

Also, the candidates who are most hardworking and most determined to get this particular position actually prepared best. And that’s exactly the types of candidates that the company wants.

Aleksandra 34:04

I agree definitely. And the company wants you to succeed when they interview you. They just really want you to succeed and do well.

How To Improve On Your Chances To Get a Job of Machine Learning Engineer at Facebook?

Natalia 34:13

So, is there anything in this recruitment process that if you had the chance, you would have done better?

Aleksandra 34:21

I would have applied earlier, definitely. And I think, generally, throughout my career struggled with imposter syndrome and particularly, coming from a very theoretical background in academia where I didn’t have a lot of experience with, for example, programming. I didn’t, in the beginning, see myself as an engineer.

I had to grow into this idea that I, you know, I would like to be an engineer. So, I would apply earlier. And particularly, when you’re just finishing your PhD or starting to think about your potential move into the industry. A really good option is to try and have an internship in a company like Facebook or Google or any other of the tech, big tech companies.

And all of them offer internships and they are not only aimed at undergrads. They’re also aimed at PhDs. And they give you a really good opportunity to check if this is something that you really want to do and that you would find interesting. Have a glimpse of how the company really works and how – what it is like to be a software engineer or a data scientist.

It can help you make the decision that you want to actually move to the industry. Not to mention that, you can finish your internship and then have a job offer. That would, for example, allow you to finish your PhD. And then, after you have your PhD you you’re in this extremely comfortable situation, when you know that you have something exciting waiting for you after, you know, after you defend your thesis.

Coding As a Means To Reach a Goal vs Coding As a Profession

Natalia 36:28

It’s also, you know, that many researchers learn about machine learning and coding because it’s a tool to reach some goal. And it’s actually important to really figure out for yourself, if you would be happy doing this for like 8 hours a day. Or perhaps your passion would fizzle out very quickly once you have to do this particular type of activity full time.

So, I know for myself that, when I was doing research projects, I was always like gaining energy when I was going through the conceptual phase. And then, losing energy when I was going through the coding and then regaining it again when I was writing and then presenting and then teaching.

So for me, I knew this was not for me. But it’s really good to do the self-discovery exercise and actually try projects; and see if you’re one of those graduates or professionals, who actually can focus on one task. But I think in many ways, you know, I think this type of job is, probably, also good for your like, long-term like development also.

In terms of your general capacity to solve problems because it allows you to focus on, on just one thing. Like I, now, do a little bit more towards business development and I have many different tasks and I have to communicate all the time. And I sometimes miss those times when I was a PhD student and I just had one problem.

And I was just sitting alone in a room just focused deeply on sometimes abstract problem. But I feel that, in many ways, my working memory was better back then than it is now, when it’s wasted with all these like little communications. Also, on Facebook, I have to say.

In many ways, I think it’s a noble type of work that is also like developing your like mental skill, mental power in the long run. I don’t know if I express myself well here, but I think it’s like a muscle. You train your brain every day to solve complex problems and in the long run, you become smarter, you know. Whereas, if you have these types of jobs that you, basically, just exchange information for the whole day then I think in the long run you’ll become stupider in many ways. That’s how I feel at least.

Aleksandra 39:11

I don’t know about that, but I can imagine with this type of job you just gain all your skills. Or your communication skills that you don’t necessarily have a chance to develop when in the more focused or technically oriented role. So, as there’s a learning to everything, a learning curve to everything.

Obsessed About Facebook?

Natalia 39:39

But ever since you worked for Facebook, honestly, did you start using Facebook more or less than before?

Aleksandra 39:50

I don’t think it changed significantly. I do use Facebook quite frequently. In the sense that I post on Facebook, like for personal updates or photos on, I think, a similar basis than I did before I joined the company. However, I definitely read Facebook more frequently than that. So, I think every day. But it’s again, it’s similar to what I did before. So, I am a frequent user of Facebook.

Natalia 40:33

So, but you didn’t get obsessed yet?

Aleksandra 40:39

No, I think I have a good balance.

Downsides of Working for Facebook: Imposter Syndrome?

Natalia 40:45

If I could now touch, also, those points that were coming up on Glassdoor often. In terms of like the downsides of working for Facebook. I think, in general, there are almost no downsides from what people say. It’s just that a few comments were just coming over often, just because it’s a large company.

And I think these are very generic comments that are typically, you know, made towards every big company. And one of them, is that there are multiple channels of communication. So, you can get a FOMO effect. And another one is that since there are so many good people at Facebook, that it’s easy to get this imposter syndrome, you’ve mentioned. And also, that it’s a fast-changing environment.

But again, what do you expect from a large, like leading IT company? I would be surprised if it’s not fast changing. So, these are actually I would say, quite like the usual suspects. There’s nothing else I can add to this list. These were the only points I noticed. But actually, do you feel that the imposter syndrome that you sometimes might feel at Facebook, is stronger or weaker than what you felt in academia? Different, maybe.

Aleksandra 42:10

I think, I generally handled my imposter syndrome a little bit better than I did in academia. But it might be also because I’m just older and I am more experienced. And I have more trust in myself than I had when I was younger. I don’t think Facebook, particularly, increased anything. But yeah, I definitely work with a lot of super smart and super talented people. And I feel very privileged to be able to do that.

So, that’s amazing. In terms of the fear of missing out that you’ve mentioned, yes, there’s a lot going on. It’s a very big company that is very active in so many different ways. There’s lots of projects, a lot of things going on. So, I can imagine why people might be feeling this way. So, what I think, particularly, helps me and helps me keep, also, my work life balance. I have a family and I have children. They definitely help you disconnect very effectively from your work.

So, even though I work from home, I have my designated space to work. And also, my mind disconnects from work when I spend time with my family. So, that I think helps me with work life balance and I think with maybe being overwhelmed with too many things. Like you don’t aspire to do everything anymore. You have a better notion of how limited time is.

Natalia 44:15

Right. I also asked about imposter syndrome also because there is the stereotype that it is especially prevalent in academia. But I have to say that, in my case, I started feeling much more of it after I left. Because, you know, in academia, everyone has their own like individual project, especially if you are in Theoretical Physics.

I was in neuroscience but I also had very like, individualistic project that was by no means comparable with projects from my peers, even from the same lab. And then, you know, how can you really compare the output? You know, you might have less papers than someone else but how can you compare if you had the same chances to publish like it’s really hard. But now, like in the open market, there are often many, like quantitative ways of comparing the performance that are like much more unbiased and clearer.

So, if you’re building a business and another business is building faster and getting more clients on board, etc., it’s like it’s a quantitative measurement. So, now I feel under much more pressure to perform and sometimes feeling much more impostor syndrome than in the times I was in academia.

So, for me, it went the other way around. Although I’m fine, but I can feel that the pressure was not as high before as it is now. So, I just want to make this point that the fact that you move to industry doesn’t mean that it will free you from imposter syndrome. Because everywhere you go, you will encounter lots of ambitious people

And the better you are at building your career, and the farther you get, and then the more of the higher the concentration of those super smart people around you. And so, if you don’t develop thick skin against imposter syndrome early on, then it will never leave you. It’s not that it will disappear at some point, it will be just stronger and stronger.

On Personal Freedom As a Facebook Employee

Natalia 46:30

Anyways, actually, I would like to ask you a few more questions, since it’s so interesting what you’re saying about working for Facebook. I’m curious about this common comment I also encounter often that the culture is open and you have a lot of personal freedom. So, could you elaborate a little bit more what that means and in what ways you have freedom in Facebook?

Aleksandra 46:48

For example, when you join the company as an engineer or I think as well as a data scientist, you go through a bootcamp process. Which is like a quick, very intensive course and introduction to the company and to the internal tools. It lasts a couple of weeks in the beginning. And after that, unless you have a pre-allocated team before you join.

You get to explore various teams that are recruiting at the moment and choose where, you know, where you want to be and where you want to join. And I found that experience fascinating. I was able to sit down with a couple different teams, get to know them, get to know the people, what they’re working on, what their team dynamics is like, and then make my choice of where I want to go and what I want to do. And that was definitely a great experience. And I think that’s one of the dimensions in which, you know, you have a lot of freedom as an employee.

On Management and Managers at Facebook

Natalia 48:03

Okay, perfect. And can I also ask you a question from another viewer. Alicia is asking, “First steps in a manager role are both difficult and vulnerable time. It’s really easy to get emotionally overwhelmed, if you develop a sense of letting your team down. I would be glad to hear what kind of support the company provides to make you successful as a manager? And what is the most helpful and valuable for you?”

So first of all, I’m not sure if you have a management experience at Facebook, so far. But perhaps-

Aleksandra 48:47

No.

Natalia 48:48

Yeah. So, if you’re a specialist, and perhaps you could tell us why Facebook managers are so effective and so likable because on Glassdoor, I’ve seen a lot of comments that, actually, the management is very professional but also friendly. And that the managers are very nice and helpful. So, what do you think? Is there any particular like style of management at Facebook that makes it so nice for the employees?

Aleksandra 49:24

It is a little bit difficult for me to answer that question, as like the original question is, I am not a manager and I don’t have that experience. However, generally, there is a very strong feedback culture in the company. So, people or the processes around it, that that are in Facebook, they create opportunities for you to give and receive feedback.

And also, there’s a significant amount of opportunities to do that on a daily basis and there are reminders about doing that. And there’s also a culture of openness and talking about difficulties openly. As the fact that you are having difficulties does not mean that you’re failing at your job. Difficulties are natural and normal.

And talking about them and seeking support it’s, I think, a very important thing. Not only as a manager but as an individual contributor in all your work. So, I think those two things, feedback and openness in talking about difficulties, might make these things; make these kinds of problems easier.

Aleksandra’s Future Plans

Natalia 50:53

Okay. So, let me now still ask you a little bit about your career path and career plans. And so, is there a career path within Facebook that you’d like to pursue? Do you have a particular plan for yourself like towards which direction you would like to go next within the company? Or is it a topic you don’t really think about, you just go with the flow and you don’t really do any planning for yourself?

Aleksandra 51:24

I, definitely, go with the flow and I don’t really do much planning for myself. So, it’s hard to answer, for me, that question. I think that’s partly because it took me quite a while to figure out where I want to be in the last couple of years since I left academia. And like I mentioned, I’ve changed companies a couple of times.

Aleksandra 51:49

I, now, changed my role and I’m in a bit different role than I was in the past. And I’m now an engineer. And I, really, am enjoying what I’m doing at the moment. And I’m trying just to focus on that and see how it goes.

Natalia 52:07

Okay, that sounds good, I think. I think over planning is actually a common mistake that people do and that leads to a lot of like, bad decisions as well. So, I think this is, in general, a better approach. I mean, I wouldn’t say that planning is bad in principle. But I think that it’s better to use heuristics rather than algorithms when it comes to your career development.

Natalia 52:35

So, the fact that you choosing a good company with a good brand and very innovative, it’s already a good heuristic. And using those like, those strategies based on heuristics it’s actually, usually, better in the long run; than just, you know, just drafting your career plan in like 20 years ahead like step by step, what you do.

So, yeah. I agree with that. Alright so, is there any piece of advice that you could give like, based on your own example, that you would like to give to PhD graduates who are now hesitating where to go next?

Aleksandra 53:19

I would say, don’t be afraid to apply for jobs, even if you think that you are unlikely to get them. The process is simple, you go to the website, you apply, you get invited to the interview and you fail. And then, you go to the website, you apply and you get invited for another interview and you fail better. And that’s how we all learn and that is how you finally land the job that you’re very happy with.

How To Improve on Your Success Rate in Recruitment?

Natalia 53:58

Like I’m curious about the spending part, so. Is there, like, any mistake you were doing initially, like in the process of applying for jobs? Like at the very start that, you know, that was a recurring problem and that you, kind of, had to get over.

Aleksandra 54:15

Multiple, multiple. First of all, I didn’t do enough. I even had enough practice in, for example, coding interviews and it ended for me. I realize you quickly forget this as a skill. So, it’s a good thing to refresh from time to time. I think there is, also, some general advice.

Like going to the interview after a sleepless night or very tired. Or sick and well, that’s some things that I’ve done and it never ended very well. That’s, I would say, some trivial things. But you, I think, find the courage at some point to reschedule your interview for a later date instead of just you know, showing up not in your best. Just the first thing out of the door. Yes.

So, a lot of issues related with like, a lot of things that you learn about communicating. How do you talk about your resume and your experiences? Particularly, in relationship to the more behavior-like questions. It’s hard to practice those questions just by yourself. And having this interview experience and having to answer them under pressure. It really helps. It helps you to just get better at this with time.

Natalia 56:00

You know, at companies like Facebook, I can imagine that candidates are, you know, most candidates already quite trained. Because they already have experience from other companies and they went through the interview process a few times. So, don’t take rejections personally, also. Because it’s always a combination of a few factors and one of them is exactly, it’s the experience part. So, very good advice. Okay. Ola, is there anything you would like to add about Facebook, about life, about career?

Last Thoughts

Aleksandra 56:42

I think the last thing I wanted to add. It’s like something that people say a lot. And it’s that a career, it’s a marathon. It is not like a short race. So, even some failures are expected and you don’t have to optimize your every decision. And with just with grit and perseverance, that’s at least what I hope for. You can get yourself where you want to be.

Natalia 57:20

And that’s a perfect wrap up of this episode. Thank you so much, Ola, for joining us today. And you guys who, successfully, came to the end of this episode, thank you so much for your attention. And if you’d like to get more of this type of content, then please subscribe to this channel. And of course, please leave us any questions you might have below and we’ll try to answer all of them. And thank you so much, Ola, again for joining us and for your time.

Aleksandra 57:48

Thank you so much Natalia for having me. It was a pleasure.

Aleksandra Drozd - Machine Learning Engineer at Facebook

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