How is it to be a female professional in a male-dominated field? Interview with Sylwana Kazmierska (known as @programistka_ai).

The story of a girl making AI topics accessible

There was a girl taking part in the recruitment process. She has become interested in the company after one of conferences. Recruiters checked her coding skills and machine learning knowledge. When asked about the details of the construction of languages, she hesitated, inquired and responded more by deduction. Could perform better. Own repository with projects to share? Although she was prepared, her code was clean and correct – let’s be honest, it wasn’t outstanding. Her resume showed a solid statistical base. She did the Stanford course as well. Clear understanding of theory but lacked experience. Is there anything she could implement from A to Z? “I think so, I just need to investigate how to do this in the Internet”– she replied immediately. It was obvious that she wanted to learn.

There was, however, something that distinguished her from the rest of candidates. The way she presented her work. She explained everything in such a way that even someone completely unfamiliar with coding would understand.
And guess what? Despite the lack of experience, she was accepted into the company. Now she runs projects and manages people, she recruits others. She made up for the hard skills, but probably that’s because she worked on the soft ones before. Pretty nice success story, isn’t it? A great example that nobody is perfect from the very beginning.

You may think that to kickstart your career in machine learning, data science or AI, you need to finish the right university, complete most expensive bootcamps, manage your website or extensive portfolio. Every year of your education spent not on programming is treated like a failure. The truth is different. If you do not have any problems with the analytical approach to the world, you are ready for continuous training (AI reality is changing so fast – 5 years ago and now are totally 2 different worlds), then you probably are ready to give yourself a try.

This story is about my today interviewee, Sylwana Kaźmierska, Data Scientist currently working for Digica, also known as @programistka_ai online. She does a great job sharing knowledge about AI, its latest developments and risks, making sophisticated topics easy to digest by everyone. She’s also a huge fan of nerdy memes, which makes me adore her activity even more! Not to be unfounded, to show the non-Polish side of readers, how well received is content provided by Sylwana, I can just tell you that having asked for the Polish professionals worth knowing in ML&AI community on one of the biggest Facebook groups in this area, her surname was one of the most mentioned.

I have invited Sylwana for a talk about some hot topics right know like AI globally and in Poland, the future and risks it brings, women’s participation in data science and her general reflections on our industry. I have enjoyed this collaboration a lot, not just because Sylwana is my first female interlocutor there. It has changed my perspective on many issues and I really would love to stay in touch with this inspiring woman and her valuable activity. All Polish readers are highly recommended to get familiar with her Instagram content and sign up for the newsletter she delivers. I hope you get as much good out of this interview as I did.


Sandra: I will hit it big from the very beginning – why artificial intelligence? What have motivated you to choose such career path? After some years, do same things motivate you or have your “drivers” changed? I have read your story on your Instagram profile but I am sure the readers would love to hear even more about it.

Sylwana: It all started from me just really wanting to start programming professionally. I had a feeling that my focused, introvert soul will find its place in this environment. 🙂 And… once I joined some Python course, I heard from mentors that “Data Science is starting to be a big thing now” – it was back in… around 2016 I think. I kept it in mind and slowly started to do some research in that field.

It turned out Data Science perfectly fits to what I always loved – solving puzzles, looking for patterns, doing research on various topics. Suddenly I could relentlessly break down cases into smallest parts to find some answers – and get paid for it! And could stop torturing my friends who usually had to listen to me breaking things down into smallest parts before. 🙂 Now I can give vent to (at least some of) my curiosity at work among people similar to me!

I had multiple motivators back then apart from curiosity, like financial stability and being able to work remotely. Now it’s more or less the same, but the more I work in this field the more I feel like I’m also in the right position to do a bit for others: like educate them on biases in AI or motivate girls to join this field and not be intimidated by the culture that says that IT is for boys.

Sandra: It’s a simple truth – the world of AI is male-dominated. However, when I have asked for a recommended and recognized data science activists in Poland on one of the communities, people have constantly mentioned your name. What are your reflections about being a woman in this field? Have you ever faced worse treatment than your male colleagues? Are there any biological or mental features typical for a women which can be beneficial in our field? Or are we equal and learn the same way?

Sylwana: I’m not an expert in biology but I have a feeling there are no major inborn conditions tied to any gender simply by looking at history. At first it was women that did programming. They were the majority in this field until mid 80s. Back then it was explained by the fact that women are more meticulous, detailed etc. Has there something changed in a way we work? I think not. In my opinion it’s rather how we’re raised and the (pop)culture, among others, that shape our beliefs and confidence in tech skills in the first place.

I’ve never faced worse treatment in my field due to the fact I’m a woman (or at least not consciously;). I heard about such cases from my friends though. I think the fact that I’m rather a statistician than a programmer (in terms of my education) might also have helped – maths and statistics are not that much associated with gender right now in my view.

By the way – I’m very pleased that my name appeared in the recommendations! And… I can imagine now all those people who visited my Instagram expecting to see solely Data Science content after those recommendations and getting memes and philosophical insights as well in-between – whops!;)

Sandra: I totally agree that memes are important but what I really love about your activity is the way you publish news from AI world on your Instagram stories. All is explained in such simple way to digest by any person. What are your sources on information? How do you manage to fight with FOMO (fear of missing out) and filter garbage out?

Sylwana: Thanks a lot!! I learn mostly from work, some “kitchen discussions”, then some internet articles. But once I come up with a topic I spend most of my time “digesting” my knowledge, filtering out “the boring stuff” and trying to come up with how to present a topic in an easy way. Sometimes it’s quite challenging – and you as a Data Scientist can probably admit that many topics are complex enough even for us to understand them, not to mention presenting them in a comprehensible way. 😉

Sandra: I know it’s a hard question and probably we’ll never know the exact answer. Nevertheless I am really curious about your perspective. How would you define intelligence? What is the difference between human and artificial one?

Sylwana: It will probably change a million times in my head but right now I see it like this: there is a tremendous number of neurons in our brains that produces thoughts and emotions. This size and sophistication is incomparable to the machines we have program right now. However I think that we see outputs of our brains as something “higher” and more lets say… spiritual because we cannot completely follow the path of a single thought. But at a deeper level we could compute it in some way – it’s just that there is too much right now for any computer to do this.

So what I mean is that I believe our brain and its intelligence is perceived as something better than artificial only as long as we cannot reproduce the brains outputs (which I believe would be possible with computers big enough) – and then it could turn out that not only intelligence but our whole consciousness is just the effect of size and architecture of this neural machine we possess.

Sandra: How do you imagine future of machine learning and AI in 5 years? How do you rate its development in Poland? Will we stay far behind? Or do you feel that we can expect self driving cars on our roads soon?

Sylwana: That’s a tricky question I’m really afraid to answer! The reason is that if we looked at predictions made on AI 5 years ago, we could see how mistaken we would be in many aspects. And now I think this unpredictability is even higher the more opportunities we see.

Anyway, I think that the condition of AI in Poland (and whole EU) will be shaped mostly by superpowers like China and US. Today we have many talents that could have an impact on global tools but rather by joining existing giants. Another interesting case would be how law would react. In my view EU’s standards will be high in terms of personal privacy and our law will stay cautious in case of AI.

By the way, I think it will also impact how soon self-driving cars will appear. I would expect them soon but first only on limited parts of some city, separate from normal traffic. Please take into account that e.g. driverless cars e.g. making shop deliveries (from Walmart) are operating like this already! So it would be the case of business – would some company decide that we are the right market to invest in? I have no idea how high we would be on that list. Would it be low (and hence far away from now) due to other European countries having more financial power? Or the opposite – us being already open to new technologies would make our market easy enough for some giant to arrange here some sort of “playground”? We’ll see.

Sandra: Seems reasonable. Not that long ago nobody has thought about applications like Uber and now taxi drivers have a great challenge to overcome. Sooner or later they will have probably the common enemy. And what are the biggest risks of AI for world in your opinion? What we, as data professionals, can do to prevent from them? How to spread awareness and set good examples?

Sylwana: From my point of view one of the big risks is trusting AI too much. For example, confining to AI models when assessing people’s credit risk, and financial services on the whole. Why? Because those can be faulty and that might not be visible straight away. Example? Employees of institutions (like every other people) can be biased. And now we train models on those biased decisions. So… the model will look accurate because it makes same decisions as people used to (and that’s basically how we validate models). And those decisions could actually be wrong from the very beginning.

Sandra: As far as I know you have completed PhD in special economics. Could you please tell us more about it? What have you worked on? What insights have you collected? And what’s more interesting – why have you decided to do it? Have you got any idea to stay at the university and teach people? What benefits from your PhD do you experience in your current role? If you have turned back time, would you decide to do this degree again?

Sylwana: I was doing a PhD in economics (with econometrics and more or less urbanism as my main fields). But actually… I resigned at my 3rd year. My friends thought I must be mad! But for me it made a lot of sense. At that stage I already knew that this path was not for me. And in economy there is this “sunk cost fallacy” – which basically means that people tend to focus more on how much they already invested in something rather than how much more there is to finish some project/construction etc. (and does that really make sense). So knowing that I mentally crossed out those years on PhD studies and asked myself “ok, so imagine you’re starting just now – what would you like to do?”.

I wouldn’t change anything – even going on PhD studies in the first place. I believe that all such experiences shape us in a good sense – but sometimes it’s just too subtle to notice at first.

Sandra: I had no idea about that! I can only imagine those comments you were to face after that decision! Let me continue this topic then. What advice would you get from the past? I already know that you do not regret giving up on your PhD. Are there any mistakes you’ve made? What have you learned through them? Is there anything you could do differently?

Sylwana: I don’t know if it’s my (learnt!) self-confidence or what, but I have an attitude like this: every decision I made was the best one in that particular moment with the knowledge I had. So when you ask me about mistakes – well, I just don’t want to be hard on myself and even interpret something as a mistake;)  And would I do something different? If you asked me a couple of years ago I would say “I wish I started programming earlier”. But the more I am in this field the more I appreciate my prior experience in other ones as well. If I went straight to programming, I would never have an incentive to ever “come back” to those other fields – I would simply not know there is something different out there.

Sandra: I really love your approach towards learning. I think that people should be more open for that, especially the ones suffering from perfectionism. Let me investigate your past more. What is the most interesting project you have participated in? Do you have your favourite domain of AI? What makes you most excited about your job? Are there any activities you really dislike?

Sylwana: For me each project that lets me go into completely new industry is fascinating. Like learning what are the patterns of rats creating their nests. Or how does the pulse show up on our foreheads. Usually I have an opportunity to speak with specialists in such field that normally I wouldn’t have and that’s also great.

By domains do you mean like Computer Vision, NLP, recommendation systems etc.? If so, I know it might sound boring but I don’t think I have a favourite domain of AI – I mean, each of them is interesting if you have an interesting case to solve. Like, I could for example say that anomaly detection is less exciting than computer vision, but then you do a sensor that placed on your ankles and knees detects if you shamble with your legs. It’s not only a great challenge but also something… meaningful. So yeah, I think that’s something that makes me really excited. Programming something new that can actually assist people.

There are things that I prefer not doing right now – like managing teams, but that’s just my preference, and there are many Data Scientists I know that love doing this.

Sandra: Totally agree! I always say that one of the reasons I love my job is the opportunity to meet great people and get familiar with their perspective and expertise. Let’s get a little dreamy then. If you had access to any data on the world, what would you work on? Is there any problem which you’d like to solve? What is your personal vision?

Sylwana: It’s not structurized in my mind yet what data precisely I would need, but I would love to do some research on how do filter/information bubbles emerge.

Like, what does it take to make a person belong to one bubble and question all other arguments? What types of personality are most prone to which type of argumentation? What does it take to reach out to someone across political lines? I think solving this issue is one of the most crucial challenges in the coming years.

Sandra: I am afraid we need to finalize this talk, although I could give you twice that much questions! I hope we’ll catch up together in together on a different topic. Our industry for sure we’ll provide us plenty of those! As usual, at the end of every interview, let me ask you… Do you have any book/s which have made a great impact on your career or life and you could recommend? Doesn’t need to be data oriented but would be great definitely.

Sylwana: I’ve received this question lately on my Instagram and I’ve been thinking about it for some time (don’t forget I’m a data scientist, I really love analysing diverse topics very thoroughly!:)). I don’t have this list yet because I feel like there were tons of sources (like articles, books, people) that resulted in who I am now. But I promise to post it on my IG as soon as I make up my mind what was really impactful in my case!

My Reflections

I’m breathless. Really. Being huge fan of Sylwana’s activity I was sure from the very beginning that it’s definitely a person who will deliver a lot of inspiration and topics to consider. I haven’t had this ease with any visitor yet to come up with questions because they’ve just been floating around in my head for a long time. The fact that I have finally found inspiring woman in data science was driving me so much but I had no idea that we’ll get to such clear message for you that with the right amount of motivation and passion to learn you can start your career on Data Science.

There are so many people who think that they’ve missed their moment. They’ve studied different disciplines, missed so many years they could spent programming. We live in a time when we fill our children with a whole week of extra-curricular activities to make them successful someday. How can we compete with people who knew from the beginning what they wanted to do in life? I love how Sylwana explains that she never regrets her decisions. Some people consider years spent on uncompleted PhD as a waste of time, while she treats it as a value. Every minute you spent on learning something new is an investment.

Data Science is multidisciplinary field. It’s not dentistry when without proper degree you won’t start your career. It’s not just statistics or computer science that creates great data scientists. They come from domains like law, medicine, or physics. It’s all about solving problems with data – aren’t those everywhere? AI is everywhere. As I’ve already mentioned thousand times on this blog, it’s impossible to be an expert in every project. Your business domain may help you to specialize. You have financial background? Learn how to develop machine learning models in this sector. You’ll for sure notice the benefits of this knowledge.


Have you enjoyed today’s interview? What are your reflections on the topics we have mentioned? I am really curious if you agree with Sylwana. Do you think human intelligence can be reproduced in future with the right means and level of knowledge? Are you able to value your experiences even if you have changed your mind in meantime? Whether you agree with her or not, please speak up your mind in comments. In case you’ve enjoyed our interview, I would be grateful if you shared it among your colleagues! And finally, if you’re hungry for more discussion, I highly encourage you one more time to get active on Instagram where @programistka_ai deals with many controversial topics like unpaid internships in data science. There is nothing better than having a discussion respectfully. W should create such opportunities because this world needs them.

In case you’ve missed latest activity, I highly recommend you to check out the interview with my previous interlocutor, Mateusz Bogdański from Arla Foods on creating great teams or relation from EARL 2021, the conference dedicated to R programming language. Cheers!

I’d never miss an opportunity to add meme. Never.

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