Diana Avalos, PhD - Data Scientist

Diana Avalos, PhD - Data Scientist at The Swiss Data Science Center

Where are you from, and what is your educational background?

Hi, my name is Diana. I am French and completed my Master’s degree in Neurosciences between Grenoble INP - Phelma and EPFL. My Master research project was conducted at the University of San Francisco on Brain Computer Interfaces. Afterward, I spent a year at NeoSensory, a California-based start-up developing sensory-substitution technology for the deaf and hard-of-hearing community. I then pursued a PhD in genetics (bioinformatics) at the University of Geneva. My research focused on better understanding genetic regulation in immune diseases. Through this work, I gained extensive experience analyzing complex datasets, and strengthened my expertise in Python, R, Bash, and GitHub, applying these tools to design and implement reproducible bioinformatics pipelines.

Can you tell me about your first role after completing your PhD and what you learned from it?

Right after the PhD, I decided to transition to industry and joined Medigenome, where I translated data-driven analyses into clinically actionable insights, ensuring the accuracy and interpretation of genetic test. This role was particularly valuable in helping me understand how to apply genetics in a concrete, clinical context. However, I found the work increasingly repetitive, as the same pipelines were applied across patients. After completing the three-month trial period, I chose to explore new opportunities better aligned with my interests and long-term goals.

What is your current position, and what do you do?

I then joined the Swiss Data Science Center. While I had good computing skills, my experience in machine learning and artificial intelligence was still limited, so I began with a three-month BNF internship, working on a project involving Large Language Models for a human rights NGO. Following this internship, I was offered a Data Scientist position. Since then, I have been involved in diverse projects, such as developing computer vision algorithms for a start-up addressing ecological challenges and applying sound processing techniques for a healthcare start-up.

What aspects of your job do you enjoy the most?

I particularly enjoy the variety of projects (ecology, healthcare), the opportunity to continuously learn new skills, and the direct, tangible impact of the work, something I found less immediate during my PhD, which was more theoretical. More collaboration with my colleagues than in my research teams during the PhD. In my current role as a Data Scientist, I am assigned to new projects with different clients each year, which keeps the work dynamic and intellectually stimulating.

Is your job related to the work you did during your PhD?

Not directly. I am certainly leveraging transferable skills from my PhD, such as the ability to learn new concepts quickly, work independently, think critically, and apply my computing expertise from bioinformatics. My experience presenting research at conferences has also been valuable when communicating results to clients. However, my current role is more focused on developing new skills in machine learning and applying them to diverse projects.

How did you adapt to the cultural and operational differences between academia and current role?

The transition felt quite natural, as many aspects are similar: I still apply data analysis in my projects, and present results regularly to my team and clients (instead of my research group and peers at conferences). I work on projects on shorter timescale (typically one year instead of several). What I particularly appreciate is the greater support and structure. I have regular meetings with the principal data scientist supervising my work as well as biweekly check-ins with clients, which provides clearer direction and feedback. In contrast, during my PhD I often felt my PI was less consistently involved, which sometimes made it harder to move projects forward.

What challenges did you face during this transition and after?

Finding my first two positions after academia was not easy. One of the main challenges was overcoming the perception that PhD graduates are not fully prepared for industry roles. Some companies treated my academic background as if I were just finishing studies, offering only BNF internships rather than full positions. This was frustrating because I see the PhD as real professional experience in a research team, where I learned to independently manage projects, think critically, and develop strong problem-solving and technical skills.

What helped you succeed in your career transition?

I reached out to many people on LinkedIn, often just to have an informal chat/ video call to learn more about their career paths and daily work, even when they were not in a hiring position. This helped me better understand which types of roles and companies could be a good fit. I also attended career events on campus and strengthened my profile by taking online courses to refine and expand my skills.

What advice made the most difference in your professional development?

The best impactful advice was to be proactive in seeking opportunities and building connections. Reaching out to people I did not know and ask them for informal chats, going to career fairs/conferences and asking questions, helped me discover career paths I hadn’t initially considered.

What can I wish you for the years to come?

You can wish me continued growth and learning through diverse projects, as well as the chance to contribute to work that has a meaningful impact in the fields of healthcare, social justice or environmental. I also hope to keep expanding my expertise in data science and machine learning.

Diana’s LinkedIn: https://www.linkedin.com/in/diana-avalos-mde/