Meet-the-Alumna │Roberta Evangelista A01 Postdoc

Name: Roberta Evangelista
Currently at: BASE Foundation – Basel Agency for Sustainable Energy
Position: Sustainability Data Science & Digitalisation Specialist
Graduated: September 2019
Degrees: BSc Mathematics, MSc Computational Neuroscience, PhD Computational Neuroscience
Graduate studies: Neuroscience

  • Tell us about yourself. What do you currently do?

I am working as a data scientist at a not-for-profit organization based in Switzerland. We work with intergovernmental agencies, other NGOs, the private sector, and financial institutions to catalyze investments in sustainable energy solutions and climate change-related projects. More specifically, I am leading the technical development of a project where we help smallholder farmers in Low and Middle Income Countries to get access to sustainable cooling solutions, which are solar-powered refrigerated containers (a.k.a. ‘cold rooms’) where they can store their produce to reduce postharvest spoilage (which can reach 70% for some crops!) and preserve the quality to get higher prices at the market. We partner with local cooling companies (who produce and operate cold rooms) and have developed a data-science based mobile application to support these companies to manage their rooms more efficiently, and to advice farmers on when and where to sell what they produce. In this role, I source and analyze data, develop machine learning models, coordinate the app development, and interact closely with the partners and farmers to make sure what we develop is tailored to their needs and the way they operate.

  • How did your work in the lab help in your career after graduation? 

During my PhD, I developed theoretical and computational models of hippocampal rhythms linked to memory consolidation, in the lab of Richard Kempter. Thanks to the collaboration with the lab of Dietmar Schmitz, which was facilitated by the SFB 1315 grant, I got the chance to analyse experimental data from hippocampal cells, performing statistical analysis and applying machine learning methods. For me, this was the first opportunity to work with real data and the first approach to ‘data science’. Most importantly, this collaboration made me understand that I enjoy working in environments where teamwork and tangible predictions play a crucial role, and that I wanted to focus on developing models and solutions whose applicability could be tested on a shorter timescale than what research can usually offer.

Together with career and personal development workshops and constant exchange with fellow PhDs, all this influenced my decision to search for a job in data science. Overall, the time I spent at Humboldt University was crucial in shaping my interest in data science and, even though my current position might seem far away from what I have been working on during my PhD, I do see it as a natural progression that mixes my background with the interests I have developed along the way.

  • How did you conduct your job search after leaving the SFB?

After submitting my thesis, I participated to a bootcamp in London, called Science To Data Science (S2DS), targeted for PhDs and Postdocs with a quantitative background that are interested in entering the field of data science. This was a crucial step to realize that these two worlds are closer than one might initially think, and that, as a PhD, you already have a lot of skills that are relevant outside of academia – you just need to be aware of it! In my case, for example, the ability to think logically and prioritize, as well as communication skills and attention to detail, are some of the skills that I have developed during my PhD years that are highly valued in the non-academic job market. Then, I moved to Zurich and looked for a job as a data scientist there. Data science is a relatively new field that is broadly defined and can cover the whole spectrum from data analysis in Excel to deep learning and big data. As such, it took me a bit to orientate across job postings and industries, but I was lucky enough to land in a position in an industry that was relevant to me (language education and travel), and where I could be exposed to a machine learning model which was constantly receiving user feedback, as well as to large amount of data. While in this job, I also started volunteering with DataKind, a global organization of data scientists, for a project to support a small NGO in Kenya to leverage the data they were collecting. This experience was great to understand that the small (but growing) field of Data Science for Social Good was the place where I wanted to be next, and was also instrumental to land in the organization I am currently working at.

  • What advice would you give undergraduate students in neuroscience?

In my view, neuroscience is a fascinating field not only because it touches the core of who we are, but also because it comprises so many disciplines and directions. Thus, curiosity is a crucial element to get a glimpse into what are the various questions different labs might be investigating, and to continuously learn from collaborators coming from different backgrounds. A solid foundation in data analysis and some basic knowledge of programming are useful skills nowadays even for experimentalist, as are the ability to communicate your research to a non-expert public. As a theoretician, I found it especially challenging to decide which technical details to leave out of the conversation so that others can understand, while staying faithful to the model you have developed or the details of the analysis you have performed. I would also recommend trying to work in different labs to see first-hand how research is done there and learn about what are open questions in the field and the main blockers, which is something that you don’t usually find in textbooks.

  • How important do you think mentoring was and is for your career development, and would you be willing to mentor a student in our consortium?

As I was doing my PhD, I realized pretty quickly that I was not very interested in pursuing a career in academia. However, I missed role models and people I could talk to about this, because everybody around me was, in fact, in academia, and there was a widespread perception that if you decide to go outside of academia, it is because you did not find a position, i.e. it was somebody else’s decision, and not your own. Participating to the S2DS bootcamp, it was liberating to see that there were a lot of people who, despite enjoying their academic research and their PhD time, as I did, had the same desire to work in a different field. Since then, I have been trying to mentor many students and PhDs who might find themselves in the same situation, or who are just curious to know what the alternatives are. After all, it is clear that not all PhD students can become long-term researchers or professors and thus, I think it is important, already as a student, to get exposed to multiple opportunities.

>>> To connect with Roberta, contact SFB Coordinator Marylu Grossman

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