Can you introduce yourself and tell us a bit about yourself and your background?
My name is Joanna, and I am a Data Science Tech Lead at OTA Insight in Ghent, where I live. I come from a small town in a rather traditional part of Poland. When I was 15, I moved out to a boarding school in a bigger city (Wroclaw) and stayed there later to study Applied Mathematics at the University of Science and Technology.
I started my first job as a python programmer at Nokia as a part-time student job and then evolved to more data-related positions. After my graduation I decided to move to France to work for a small company (Heetch) and spent two years in Paris. In August 2019 I moved to Belgium – mostly for love, but also because I found a good career opportunity 🙂.
What is your job and what does a typical workday look like for you?
So maybe a few words about the company I work for – OTA Insight. It’s a big-data company providing hotels with tools to make revenue management easier. We have a set of products going from a dashboard to compare their prices with their competitors through a tool to analyse internal revenues data to a “market-focused” application to analyse the overall demand levels.
I am a Data Science Engineer (and since very recently also a Tech Lead) and a Technical Product Owner. So currently, I have two types of things to do: one of them being data and algorithms research (together with the team) and the second one working on product improvements around Market Insight (which is our newest product and is fully data-science driven). The data research part focuses on the hotel industry problems which we try to solve using statistics, probability and machine learning techniques. The second part of my responsibilities is to take part in the product related brainstorms and discussions followed by working with the engineering team to implement the solutions in the tool.
My typical day starts with a “stand-up” (which is not really standing) with my team, which is just 3 people. We talk about the tasks of the previous day – usually showing some results and getting feedback or brainstorming when we are stuck on something. We also discuss the plan for the new day and then we go each in one’s direction, pinging each other whenever we need to “borrow a second brain“.
My company collects a lot of data about hotels prices and availability but also about different “demand” signals: like hotels and flights searches. We try to figure out different ways of showing hotels how to optimise their pricing and marketing strategies. We often also work on many smaller projects for big hotels chains or internal projects to improve the functioning of our products. It’s a lot of trial-and-error approach and going “out of the box” to come up with something new.
When it comes to technology, we have a pretty neat stack: data stored in Google’s BigQuery, the team working with python in jupyterlab versioned in gitlab.
Joanna’s team at OTA Insight
How about when you’re not working? Any hobbies or interests you’d like to tell us about?
So I have two main hobbies – one of them is travelling (I love to visit European cities and learn about their history) and another one is balfolk dancing. Balfolk is a kind of “modernised” version of standard folk dancing. So no traditional clothes nor dancing on a stage. Rather relaxing festivals all over Europe and meeting amazing friends there. If you’re interested how it looks, I think that’s the best overview: https://vimeo.com/185192570
Joanna dancing Balfolk
A third one, which actually didn’t start as a hobby but rather an obligation at school are languages. I now, besides Polish of course, speak English, not bad French, I am learning Flemish and still have some German (from school) in my head. And I must say I love the way how European languages are connected in often funny ways and I am pretty sure that Flemish will not be my last one to learn! And as you can imagine, two main hobbies are suspended these days, so I end up spending most of the time on languages.
What or who got you initially interested in coding and / or pursuing a career in tech?
As a child I was always good at calculus and was lucky to have amazing teachers, who happened to be (up till high school) all women. I think this was a key factor to show me that mathematics is also for girls. Then it went pretty smoothly – I got lots of support from my mom and got into a high school with a lot of focus on math. I knew since forever that I wanted to do something with math – didn’t really know what.
I had quite some programming at school, but it was C and C++. Even though I got the concepts pretty quickly, I never enjoyed doing this. The breakthrough came really by accident – during my first year at the university I got a course called “introduction to programming” and we had python there. I really found it so nice and intuitive to transfer your thoughts into code that I fell in love with it. After the course ended I became aware that there would be no further programming in python in my curriculum.
So… I wanted to learn more myself. But you can do everything in python – and I didn’t know what to choose to start. So I took a very pragmatic approach and wanted to learn whatever would bring me a job later – so I’ve send my CV (with almost nothing in it – some call center job and a just started university degree) all around with no plan to actually get a job, but only to see what they would advise to learn.
I got a few calls saying that I don’t have enough experience to hire me, but I used them to ask what kind of exact skills they would need. That was already helpful, but I was really lucky to… get a job, or actually a student job, as a python programmer. And there they taught me almost all the coding I know now. So in the end, what actually pushed me into coding was my enthusiasm, chance and some people at Nokia who actually believed in me.
How did you navigate your way to Data Science?
I read a lot online. That sounds simple but at the same time is not simple at all. It took me a while to go over never ending blogs and websites to get an idea of what to learn and how.
If you look back on when you first started out. What advice would you give yourself?
I think I would tell myself that work-life balance is important and that it’s ok to say (especially to yourself): I can’t do it that fast – it is too much, I overestimated myself. In my first years of work I often worked long hours not because I had to, but because I didn’t want to disappoint myself. I still tend to do this from time to time, but it’s something I am working on.
Are there any world changing data problems that you would really love to work on? (Extra question from Christina
I was reading recently about reforestation projects to fight against climate change. It seems to be a good way to “compensate” for our CO2 emissions. I would love to work on different parts of this one day – maybe working on some models to decide where to plant trees and how or help with developing already existing ideas.
Do you have any favourite resources or projects you like to follow?
Channels on youtube I follow:
Last Week Tonight with John Oliver
CGP Grey on youtube
I really like to read xkcd, but this one everyone knows I guess. https://ourworldindata.org/ is also cool. For professional stuff I try to read the blogposts on Kaggle from time to time and https://towardsdatascience.com/ .
Any topic you would like Last Week Tonight to delve into?
I would love to see John Oliver trying to explain advantages and disadvantages of AI in the public sector. Like – not too high level, but also to make it still understandable for non-tech people.
What made you join the women.code(be) community?
When still in Poland, I used to volunteer for a while in an organisation called Geek Girls Carrots, where I organised a few workshops and meet-ups. Now, in Belgium, I was trying to find a similar community before I would go on with starting a new one myself and I found women.code(be).
I really hope the moment the pandemic is over, I will be able to organise some coding workshops in collaboration with the women.code(be) community.
What kind of workshops did you organise for Geek Girl Carrots?
I organised two kinds of them: fundamentals of SQL and basics of python (one or more editions each). Both of them were weekend-long workshops with a big focus on practical skills – kind of from zero to hero 😉.
The whole idea was to first find a group of professionals who would like to spend one weekend teaching people (mostly women) non-profit. This was surprisingly easy – the community I studied and worked with was very enthusiastic and I managed to gather a group of 8-10 mentors for each workshop. Then of course we had to find some sponsors and locations, but considering the cause – getting more women in IT and the rather low cost of the events, that was also not really a problem.
Together with the mentors, we prepared instructions for the participant’s computers setups, the presentation and later home assignments.
The formula of the workshops was to go over all the basic concepts with the participant, following what was presented. The next step was solving tasks using the freshly gained knowledge. For me personally, it was very important to have one mentor for every 3 to 4 people. So you can make sure you have enough capacity to help everyone with their assignments.
After a very intense weekend of coding, we were splitting participants into small groups with one mentor each. Then we would give them a bigger task to work over the next coming week or two – just to retain the knowledge and have a project to talk to during future job interviews.
How could the tech industry be more inclusive for women and minorities?
It’s a fact that there are not many women and minorities in STEM fields. Which I definitely don’t like and I am not sure where it’s coming from. A lot of research is being done on what is causing this and I hope that with more and more data we will get to the source of it. I am still trying to figure out what I should personally think about it.
I am very pragmatic – I think the companies should always be objective and just hire the best candidates. The problem starts earlier though – already. There are definitely a few things which can be done by the tech industry which can improve the “ratio“. Focus on unconscious bias, supporting communities and early education
There is still a lot to be done about unconscious biases and this is something that every company should be wary about when training the interviewers. It’s hard work to even realise your biases and even harder to fight against them, but it’s definitely worth it.
Another thing is trying to support communities and organisations which try to popularise ICT among women and minorities. Spreading awareness and leveraging role models can be very helpful.
In my opinion the biggest work is to be done on the education level. Improving the general understanding of young girls that ICT is as good a path as every other when thinking about “what do you want to do when you grow up“. Role models play a very important part in that and I find this set of interviews very interesting 🙂.
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