There is no "one-size-fits-all" solution in Data Science

Hey, my Name is Gabriela and I will give you further insights into my career path and my job as Junior Researcher.

You go to work on Monday morning, what does a typical week look like for you? What are you working on?

First, I come to terms that the weekend is over! 😉

My team has a daily meeting each morning. Before that I go through all my tasks and make a plan for my day and a general plan for the whole week. In such manner I can prepare questions or check, if there are any concerns. My work really depends on the project I am working on. On the one hand I am  programming, and on the other hand I do research on how to solve a problem I currently have.

Which professional project do you particularly enjoy looking back on or are you currently enjoying working on?👩‍💻 

I am working on a platform – called EXPLORE – which allows users to investigate and deeply understand their data. More specifically, I am working on it's use cases.

Recently, I worked with Formula 1 telemetry data, obtained from a video game, to demonstrate the platform's ability to process a real-time stream of data. Of course, the project included data collection, which in this scenario meant playing the game.👾 I was able to work on all aspects of Data Science and do the whole pipeline. It really felt like a playground, since I also processed the data and influenced it.

What are the challenges of your job?

• There is no "one-size-fits-all" solution when it comes to Data Science projects. Every dataset is different, and needs an unique approach tailored to it. After some time you get used to it and really know how to manipulate the certain data, but then you start a new project – and the whole process starts again.♻️
• So it's really important to be adaptive. Experience plays a huge part, since you are able to see similarities between different projects – and can predict possible problems and adapt your approach on time.
• And of course, as a part of the IT industry, you always have to be in the loop with new technologies and inventions. But that is the fun part of it. I don't percieve these challenges as something negative, it's constant improvement which is I what I'm looking for.

What do you like the most about your job?🥇

I love the certain level of freedom I have, when it comes to choosing my own approach to a task. It's also a huge reason why I like Data Science in general. The majority of problems can be solved in multiple ways, and I can pick the solution which I like the most, and do it my way (some constraints do apply).

It's a place to be creative. In fact, data can be innovative and you can work on projects, which will be helpful to people and make things easier on the long term.

How much do graduates have to know when they start their careers? Is the knowledge from your studies sufficient or is the knowledge used in companies so specific that you have to acquire it anyway?

There will always be specific skills concerning the company one has to acquire, but that is expected.

Knowledge from studies is sufficient for all the general tasks and important for gaining a base understanding of everything. Usually someone's work will be focused on one detailed area, which is hard to cover during studies. In addition to that, companies have certain tools they use – so you also need to learn those and adapt to them. 💼

Which knowledge and subjects from your studies are you particularly happy about today? And why?

Besides the Python experience I got throughout different projects, I am mostly grateful for learning more theoretical aspects of Data Science, and different algorithms. It makes it so much easier to keep an overview of what is used in the industry and research sector.

What knowledge do you need more today that you first had to acquire? 

The skill I had to learn on my own is basically to investigate problems and search for the right answer

💡 There are often jokes on programming something and just looking for the right solution online –  whereas most of the times the found solution needs to be adapted to the problem you're having, so you really need to understand it yourself. Also, when it comes to finding a code answer, debugging is a skill on it's own, and it takes time to improve it. In addition to all that, it is important to know when to ask for help. 🙋

Your assessment of Bachelor, Master & PhD degrees: What degree is absolutely necessary in your area and are there differences in the positions?

It really depends on the job position. Regarding IT, it is mostly completely fine to be self-taught – knowledge is the most important.

However, going to university makes getting that knowledge easier. For certain areas, degrees are necessary. When it comes to "higher" positions (like technical leads, managers) a Master is needed, especially for research positions. PhDs will not always be necessary, but are definitely a huge plus point. ✅ 

During studies: Focus on topics you are interested in. Grades really aren't that important. Instead of spending time on things you do not enjoy, just to get a better grade, focus on the areas you love and want to work on in the long run. It definitely saves time!⏳

After studies: Apply for the jobs you like, even if you feel like you might not be qualified enough. Maybe the company has a role with a better fit for you. But of course, don't take it to extremes. 

Do you want to know more about Gabrielas exciting everyday work at Virtual Vehicle? In the latest episode she talks about creativity and how she is dealing with situations, when her research doesn’t work out.

👉 Have a look at our Talto Talk with Gabriela.