Rosana de Oliveira Gomes, Ph.D in Astrophysics and Data Scientist, on how to transition into a data science career.

What job did you get?

Next spring (in Europe), I am starting to work as a Data Science and Innovation Assistant for an Austrian company specialized in Time Series. The work will be done within the Research and Development department, with applications to energies, among other topics.

 

Can you describe your journey into data science? And why did you switch careers?

As a Ph.D in Astrophysics, I’ve always loved doing research and finding interesting insights about stars and Nuclear Physics.

In 2015, I came to do part of my Ph.D. in Frankfurt (Germany) at an interdisciplinary institute. There, I got in contact with many physicists working with AI and Analytics applications to several fields, such as neuroscience, finance, and renewable energies. Also, around the same time, there was a lot of turmoil about the Climate Change and the Paris Agreement.

I remember how excited I was to see how many applications to society someone with a similar background as mine can have. To me, this was the moment that slowly but steadily I started my career in data science. First by attending seminars about AI to just learn what was it all about; then later by joining courses at the university and online, and starting my self-learning path from books and study groups.

My interests in AI and the possibilities to help society grew as time passed and, at some point, it became clear that my sort of hobby became more important to me than my current research. So in 2020 I decided to finally take the leap and joined Omdena.

 

Can you share a point in your career where things got a bit difficult? And how did you overcome roadblocks? What mindset related tips can you give?

Deciding to change careers after 10 years of doing research was not easy. Many people could not understand why I wanted to leave my research path and some even thought that I was going through a career crisis.

That was when I learned that mentors are crucial in every professional endeavor. The person who supported me the most in my career change was my Postdoc supervisor, who encouraged me to take the time of my research to learn about other fields where I could contribute to the world, such as renewable energies and deep learning.

Unfortunately, my mentor passed away rather abruptly in 2020, after being diagnosed with a rare disease. The loss of someone who would support me in my decisions and give me space to explore was brutal. Despite all the hard situations and even the fact that I was getting even more involved in my research field given the new academic circumstances, it became clear to me that the path I was following was not the one I wanted to pursue. But even though it was hard, I knew that someone believed I could do it. And so I did.

I believe that the best we can do to ourselves as professionals, is at some point decide to own our own story. It does not matter what other people think is the right for your career, and even how much stability or money you can make. In the end, you have to listen to your own passion. To me, believing in what you do is the only way to be happy. As my mentor told me long ago: ‘You will be working many hours of your life: it better be working on something you enjoy and care about!’

 

Being a Women in Data Science, do you have any advice for our female changemakers?

We grow up in a society that still says what women should wear, how they should speak, and what kind of jobs we should take. Knowing who you are and where you want to go is a muscle to be exercised. There will always be people doubting what you can do, trying to put you in lower positions than you deserve, or giving the credit of your work to male colleagues.

It is important to understand that it is not a war in which opposite genders are enemies, but a matter of knowing your value as a professional regardless of it. My advice to all female changemakers out there is to make sure that the place where you work is a place that values people equally. Always pay attention to how men and women are treated in the environments that you choose to put yourself into. Are female data scientists in leadership positions? Do women receive recognition about technical work or are they doing mostly management? Are the opinions and ideas of female employees heard and acknowledged?

 

How did the Omdena experience help you in securing the job? How can someone in the Omdena community make the best out of this experience?

Omdena was the game-changer in my data science story.

First, it brought me great opportunities to learn and get experience with Data Science while fulfilling my personal values of making a difference in the world through my technical skills.

When I decided to leave academia, I quickly found out that it would be impossible to get a position in the industry without real-world project experience, even holding a Ph.D. Thanks to Omdena, I could tailor the choices of projects I wanted to be part of — related to sustainability, energies, and the humanitarian sector — which eventually made me an experienced data scientist in these fields of applications.

Through my currently 8 projects with Omdena in the past year, I had the chance to gain experience in a broad range of AI techniques and industry practices such as predictive modeling, computer vision, natural language processing, as well as building APIs and deploying models in a Talent project. By working with large international teams, I could also improve my communication, management, and mentoring skills acquired from academia and apply them within Data Science projects. In every single interview I took, I could use these projects as examples of some experience or skills achieved.

Another incredible advantage I had by working at Omdena projects was to get to know a lot of like-minded data scientists. At the end of each project, I would end up with new friends and colleagues that helped me along my journey with tips, recommendations, and endorsements of my skills. The community, together with the constant Omdena support, helped to establish me as an experienced Data Scientist to the point that when I applied to my new position, the company already knew me.

My advice to the new members of the Omdena family is:

  • Choose projects aligned with your values and that are in a field of application that you would like to pursue (for example, finance, healthcare, mobility, etc).
  • Make sure that you walk out of each project with a new set of AI skills. It can be quite easy to only apply to projects with techniques you already know, but the beauty of learning with 50 (initially) strangers is a very rare experience for you to miss.
  • Connect to people in the community and build your own network of changemakers. They will be your support system all the way and maybe even become friends for life!

 

If you would start all over again. What would you do differently?

I’d definitely spend less time ‘on the fence’.

When you are changing careers, it is really important to embrace the new path. I spent a very long time identifying myself as a ‘Researcher in Astronomy changing careers into Data Science’ rather than calling myself a Data Scientist, even when I was working in Data Science projects for a very long time.

 

Any closing words?

Whatever path we choose in life, it is extremely important to have a support system, so we can lift each other up and celebrate every single step of the way. The same is true for careers in data science.

Find your kind of people and make sure to have fun with them on your data science journey. They will be the friends who will spend days with you trying to figure a way to improve models performance, who will recommend the best new AI courses out there and who may be one day the ones that will recommend you to a wonderful job.

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