Tell us about your background.
I am from Barcelona. My dad worked as an IT technician, so I was always surrounded by PCB boards and microcontrollers. I attended the technical higher education in Barcelona, where there were only two girls out of 15 students in my group. I decided to study for my bachelor’s and master’s degrees in industrial engineering after considering architecture and political science, truly a last-minute choice one hour before the enrolment closed.
I initially wanted to work as an F1 mechanic, but then I discovered I was more interested in renewable energy. I finished my master’s in Barcelona at UPC-Barcelona Tech. I didn’t know what I wanted to do, so I started working as a project and business manager in a research centre, writing grant proposals for EU-funded projects in power electronics and electrical engineering for integrating renewable energy sources. This is how I got in touch with academia, and in 2017 I started my PhD in electrical engineering focused on integrating renewable energy sources in distribution grids (electric vehicles, solar photovoltaic in houses, energy storage systems), how this could be forecast and the potential impact in the electrical grid.
How did you get interested in data science?
I have always been fascinated with people who write code, but I never felt like I could do it. I also had a bad experience at university where a professor told me that no matter how hard I would try, I would never code as good as a boy and that we are slower at learning and coding. So, even though it was something I really wanted to try, I always thought I would never get good at it.
However, during my PhD, I became the lead teacher of a course called “Control and Automation for the Efficient Use of Energy”. I was told to re-design the course, and together with a friend, I got my hands on Arduino and Raspberry Pi, and started coding in C++ and Python. Initially, I was so afraid to destroy the microcontrollers, but I decided to surround myself with friends who supported me and helped me create the course. As a result, I saw I really enjoyed coding and teaching, and the course became a success.
Then, I found myself reading articles about Python programming and data science, and I realized I wanted to focus on that for my PhD. I wanted to be able to create a forecasting algorithm myself. So I decided to change my PhD topic to include forecasting for renewable energy sources. I became a little bit obsessed, and I took all the courses I could take, spending holidays, weekends and nights on it. I asked for grants and funding to cover the expenses of the courses due to the economic limitations of being a PhD in Spain. But I remember it as one of the best decisions I ever made.
I went to a data science workshop in Denmark organized by EIT InnoEnergy and DTU, where I saw that it was exactly what I wanted to do in the future. I had the opportunity to attend a coding boot camp in Barcelona, where I made many friends, and I had the chance to be in a jury afterwards. I also participated in a Python workshop organized by ASPP in Italy, where I learned a lot of good practices when writing code and participated in a coding challenge within a team. There was a lot of learning and a lot of fun. In these courses, I had women mentors who were super smart, fun and caring, and I learned a lot both personally and technically.
What are you currently working on?
I am working as a Data Scientist in a Danish company called ConWX. This company’s core business is on weather and power forecasting for wind and solar. I joined the R&D team in August 2021, and I’m in charge of the icing forecast for wind turbines, decarbonization of oil rigs by integrating energy storage systems and behind-the-meter PV forecasts. I am also interested in the engineering side, such as monitoring ML models, putting ML models into production and setting up the APIs to have an end-to-end solution with our ML model. One of the things I enjoy most is seeing how our models are used by our clients daily, and how our job tries to help mitigate climate change.
How did you first discover WiDS?
I discovered WiDS on a LinkedIn post. To be honest, one of the things I struggled with the most was feeling that as a female data scientist or engineer, I would be the only one in a tech team, conference or workshop. This is why having role models was one of the essential things to see I could be part of that group and that I could also be good technically, but that there was no need to be perfect or the best. Reading the interviews helped me realize that there are a lot of women working in this field and that they are amazing technically.
Have you been involved with WiDS since that first experience?
Yes, first I tried to be a speaker in the local WiDS in Barcelona. Afterwards, I had the opportunity to attend the local Women in Data Science (WiDS) Copenhagen in April 2022. For me, it was one of the best experiences I had because it was the first time I attended a technical conference where all the speakers and most of attendees were women. And sitting there listening to their journeys, how it has not been easy most of the time, all the technical knowledge they have, their coding skills and their ability to speak in front of an audience made me feel like I belonged there.
How has WiDS made an impact on your life and/or work?
First of all, it made me realize how important it is for us to share what we are doing with the world, even if we think it is not relevant. We can always be a role model for someone else, and it is important to speak up, share what we do, how we do it and start working in our network.
WiDS opened a lot of doors in the data science world for me in Denmark. I met a lot of software developers, engineers, and data scientists. I also got to know other organizations such as KEA, the Danish Data Science Academy and Co-coders.
What comes next for you? And what are your hopes for women in data science in the future?
I always have the feeling I need to learn more, that there is a new ML technique, a new Python library or a new MLOps tool. I would like to continue working in a team where I can learn and grow, implement new techniques, get better at the engineering part of data science, such as monitoring our algorithms and creating scalable solutions, and of course, see how our solutions are being used in daily operations.
I would also like to be more involved in the data science community and be part of datathons and coding workshops because this is how I started learning, and I would like to contribute in the same way.
My hopes for women in data science are that they do not think they have to be perfect to deserve being in the field. We need to share our strengths, successes, and failures to learn and get better at it. It is only by trying that we get better at something. So one piece of advice would be to get out of your comfort zone and try what you really like to do.
How has participating in WiDS impacted you? Send your #WiDStory to: firstname.lastname@example.org.