We "interrogated" this time one of our other Fellows who finished the first Asigmo Data Science Training Program back in November 2020. Rodrigo Da Matta shared his knowledge and wisdom on the perspectives of the work in the research institutions, his future career aspirations, ethics in Data Science, and the necessary skills that the new batch of Data Scientists ought to have to be able to navigate in this (occasionally!) thorny career.
People are doing marvelous things like self-driving cars, automated translations, and computer vision applications to help medical diagnosis. But the algorithms are not perfect, they are not bias-free.
Asigmo: Hi! First of all, please introduce yourself, where you are from, and what you have been doing so far, and how you ended up in Asigmo Data Science Training Program.
Rodrigo: My name is Rodrigo da Matta. Born and raised in Brazil. Now I’m living in Finland for about 2 years. Previously, I was working in Academia, both lecturing, and doing research. I have a master's in Civil Engineering with an emphasis on Oil and Gas Reservoirs. I started lecturing, both for engineering and IT undergraduates just after completing it. It was during that time that my interest in Data Science began. Back then, I was very excited about the teaching career; I enjoyed not only giving lectures but also increasing students' learning experience. I remember being invited to teach a Statistics course and I wanted to do it differently, not only seminars combined with exercise classes and exams. So, I suggested and got approval to have our classes in the computer lab.
A little before I got interested in a programming language very suitable for Data Science, R. I started doing some online courses and I was quite impressed about how powerful it is. Its syntax is also very easy to grasp. I remember that one of the requisites of those courses was to use GitHub to publish our assignments for peer review and an international recruiter saw my profile there and emailed me asking if I was interested in a Data Science position in their company. It was about 4 years ago or so. And of course, I wasn’t expecting that, and even prepared for such. But it definitely caught my eye and made me think about a change: I always liked computers, enjoyed Statistics, and then I was delving into coding. After some reflection and planning, I decided to quit my job and pursue a career in Data Science. So, I ended up doing another master's in Finland, this time in Analytics.
Asigmo: Why did you choose Finland actually?
Rodrigo: This was quite interesting because I didn’t. Finland chose me (laughs). For most Brazilians when they think about studying abroad, the USA is the first place that comes to their minds. Canada may be the second, and then some western European countries like the UK, France, and Germany. But I was very keen to get acquainted with the Nordics: my master was in the petroleum domain and included data from a Norwegian oilfield. My former supervisor always suggested being familiar with Norway since it is a big producer of oil. But I wasn’t sure if the oil industry was the right place to start a path in Data Science: it is an industry that due to its size reacts slowly to technological changes. As an alternative to Norway, there was Sweden. Then I applied for a master’s there. I was accepted but they were not able to offer me funding. So, I waited another year and applied again for the Swedish programs and in parallel to Finnish ones, just for the sake of trying. Finland is next to Sweden anyway. In the end, I was successful on both but again Sweden wasn’t able to offer me funding but Finland did. That's why I ended up here.
Asigmo: Do you think there is a lot of potential for Data Scientists in the Nordic countries specifically?
Success in education often brings innovation.
Rodrigo: I think there is great potential everywhere since we see nowadays a great interest in digitalization. Every industry will be digitalized in the end, and when you digitalize, you end up with more data. So yes, I believe there is a great potential for Data Scientists in the Nordic countries. People from there are very well-educated. And success in education often brings innovation. For instance, Finland is quite a big country by European standards, but it was a poor nation just a few years ago. Finland struggled after World War II, and they decided to value high to properly educate their citizens. The Finnish experience towards a world-class education was very successful. We may say that today Finns have a high standard of living and innovative companies in fields like digital games, finance, telecommunications... Even the old industries like pulp & paper and timber are embarking on digitalization at a fast pace. So, there is definitely a serious need for Data Science to support such digitalization and create value from data.
Asigmo: So, you joined the Asigmo Data Science Training Program. What kind of expectations did you have? Why did you join it and what kind of skills did you want to gain and what kind of skills did you gain?
Rodrigo: I joined for basically 2 reasons, the first one was to gather more skills to supplement those I gained during my last master's. When I saw the description of the program, I immediately saw that some topics were missing in my curriculum, like Natural Language Processing and Machine Learning Ops. The second reason why I joined was the support of Asigmo in landing a job afterward. I am currently in the middle of a career change: I was an academic, a researcher, and now I am trying to make this transition into Data Science.
Asigmo: Why would you like to change from Academia? There is still a high demand for Data Science skills and modeling in academia.
Rodrigo: It’s an interesting question. Back in my home country, there is a hard boundary between research and education per se. For instance, most private universities are more focused on training. Of course, they have some commitment with the Ministry of Education to producing research. But it is not their main orientation. On the other hand, there are public institutions like state universities where the research component is strong. Either way, one cannot progress much in Academia if does not show relevant results, such as published papers. The thing is that I found it quite hard to land a research position by becoming a professor in a state university. You have to wait for a public call and then go through an exhaustive and time-consuming selection process. Worse, the assessment criterion is not quite clear and, in my opinion, not fair. I was kind of bound to work for private institutions. So, I wasn’t able to progress much in research and have the tenure to stay in Academia. Therefore, I decided to go on sabbatical, equip myself with more skills, and transition to another field. And Data Science seemed to be the best choice at the time. I remember to read once “Data Science, the sexiest job of the 21st century”. Well, let's see how sexy it is.
Asigmo: Do you think Asigmo mixed really nicely with already existing skills and opened you up to something new? Do you think that the skills that you got during the program will help you out with the career in the private sector?
Rodrigo: Definitely! Before I joined the program, one of the skills that passed unaware to me, and I'm grateful that it was one of the topics in the program, I mean, SQL. It was the subject of the second week of the Bootcamp. When you check the requirements of job ads most of them ask for this skill, which means, be able to perform queries on relational databases. Most of the data out there are unstructured, but it has to be gathered, parsed, and structured as relational data in some sort of SQL-based database or if it's Big Data using Spark. Before the program, this information was quite obscure for me. And after doing the program and getting acquainted with the bits and bolts I understood how important it is.
Asigmo: Sounds great! The next one is more of a future vision question: people say that you have to have both strategies for your career's development and seize opportunities when they come around. So, in terms of strategy, for your career, what would you be your short-term and long-term plans? In the most positive way.
Rodrigo: Well, this is an area where you have to constantly evolve to be successful. Now I feel that the roles in Data Science are changing, and one has to harness more skills such as being able to engineer the data. More and more companies tend to deploy well-finished data products, and just knowing some modeling is not enough. A Data Scientist would be half complete if he or she doesn't understand that. That’s why these Data Engineering skills are important for those who are aiming the Data Science field, including me.
In 5 years time span I see myself working as a Senior Data Scientist, leading teams and sharing the expertise that I grasped in the past and during the program, spreading the knowledge. I believe that after being an instructor I will carry this education seed forever. And updating myself continually — so in a place where I can grow and help others.
Asigmo: That sounds like really great ideas and goals! What would you recommend to people who want to become a data scientist and who for instance would want to apply for the Asigmo Program? What would you have advised yourself back then when you were applying? What skills to consider, which books to read or videos or courses to watch?
New ideas, algorithms, models surge every day and if you are a button pusher you will not be able to go to the next level of the profession.
Rodrigo: I would say to a person who wants to enter at this point the Data Science field: you must understand well the algorithms, what happens behind the scenes. The area is evolving fast and becoming effortless to deploy a model, I heard recently a new term— “Citizen Data Scientist”. This means, it's becoming untroublesome to do the tasks, there are lots of tools to help you. It will demand fewer and fewer lines of codes and things are becoming automated. Now we have AutoML — Automated Machine Learning. So, I think it will be easier— if this trend continues— for newcomers to enter the field. But this could be also dangerous because if you only push the buttons, it will be a black-box for you. So, it is important to be prepared to deploy something from scratch, pick a paper, understand it, and make the best use of it. New ideas, algorithms, models surge every day and if you are a button pusher you will not be able to go to the next level of the profession.
Asigmo: As the last question, based on what we have discussed before, what kind of future do you see, what will be different?
Rodrigo: It's an interesting question. What comes to my mind is that now there is a lot of data and a lot of computing power to process this data. People are doing marvelous things like self-driving cars, automated translations, and computer vision applications to help medical diagnosis. But the algorithms are not perfect, they are not bias-free. There are also ethical issues when you deploy those algorithms and base your decisions on them without any further updo. I became aware of this, also during the program, where we were exposed to some past issues. It was a week about AI ethics and I deem it now as super important. If we do not understand and deal with it properly it can be catastrophic!