What did you study at A-level equivalent and why did you select those subjects?
I undertook my studies in Spain, where the compulsory subjects were Castilian language and literature, English, and then either History of Spain or Philosophy (I chose History). For my specialised subjects, I chose Biology, Chemistry, and Mathematics.
What degree/PhD did you study?
I completed a Licenciatura (5-year degree) in Biotechnology (in Spain), followed by an MSc and PhD in Systems Biology at the University of Warwick, UK.
What inspired you to choose and study your degree subject?
It was very difficult for me to choose a degree, as I have always had multiple interests. When I was considering options, I felt a bit lost. Nobody in my family had gone to university, and I had limited advice in my immediate network. I wasn’t aware of biotechnology at the time. While I liked biology as a subject, I wasn’t very convinced about it as a career option—I wanted to pursue something with real-life translation potential. At one point, I was trying to decide between psychology (inspired by various aspects of philosophy and anthropology we had studied) and chemistry (yes, quite different!).
All this changed after a school trip to a careers fair, where I discovered biotechnology. It was perfect for me—it had biology as its backbone, plus loads of maths and chemistry, all geared towards real-life applications of research skills across different fields. A bonus was that it was available in my hometown, which made it the only financially viable option at that point.
After my degree, I couldn’t afford to do a master’s right away, but I wanted to pursue the master's/PhD path. I worked for a year as a research assistant and used that time to look for opportunities that would help me develop a different skill set and provide funding. The Systems Biology DTC at the University of Warwick was an amazing opportunity. It offered a 1+3 funded MSc and PhD and gave me the chance to develop programming and big data analytics skills.
What key skills did you learn at university?
Beyond academic and subject-specific knowledge, I developed leadership, teamwork and public speaking skills. I also learned to plan for both work and fun, and I realised how important social engagements are for building your network and enriching your university experience.
What jobs have you had during your career?
I had various jobs outside science to support myself financially as a student—some bartending, tutoring, and several substantial translation projects. Within science, I’ve worked as a lab technician, research assistant, fellow, data analyst, data scientist, manager, and now director of a shared research facility.
What is your current job and what do you enjoy about it?
I co-direct the Computational Biology Facility (CBF), which is part of LIV-SRF. I lead a team of around 15 data scientists and software engineers to support a wide portfolio of projects with data and software components that help answer big questions in life sciences research. I also carry out my own research through a few projects, with a particular interest in increasing diversity in data careers.
I love the variety of science we get to contribute to as a team, as well as working closely with my team—an incredibly talented, kind, and interesting group of people. The CBF is like a mini company within academia. We manage multiple projects, monitor finances, engage with new users, and more. It’s a complex but very exciting environment to be part of.
Do you have an area of expertise?
Yes, I specialised in multi-omics data analyses with interests in biomarker discovery using machine learning methods. I am also interested in research culture in biomedical data science—specifically, how we can break down barriers and improve working practices in interdisciplinary teams.
What has been your most exciting project?
There have been many exciting scientific projects. Recently, I worked on a biomarker discovery project using proteomics to predict a fatal disease. We achieved outstanding results and are eagerly awaiting further validation.
However, the most exciting part of my job is developing my team—seeing them grow over the years and planning together how we can continue to improve and stay current with new technologies. I’m also particularly proud of our outreach work, which focuses on exposing young people from underprivileged backgrounds to data careers, improving women’s visibility and access in the field, and creating new opportunities for our master’s students.
What are your top tips for working in your industry/sector?
Here are my top tips for working successfully in biomedical data science, especially in today's rapidly evolving landscape:
- Develop robust critical thinking and analytical skills: With the rise of generative AI and automated tools, it's more crucial than ever to critically evaluate results and question assumptions. Cultivate a healthy scepticism and validate findings through multiple approaches.
- Build and nurture professional networks strategically: The interdisciplinary nature of biomedical data science makes strong professional relationships essential. Engage with clinicians, researchers, statisticians, and fellow data scientists. These connections foster collaboration, knowledge exchange, and career growth.
- Embrace intellectual humility and collaborative learning: Recognise the boundaries of your expertise and be comfortable saying "I don't know" when faced with unfamiliar territory. This field requires continuous learning—from molecular biology to clinical practice to advanced computational methods. Encourage a culture where asking for help is normalised and supporting others' learning is valued. This vulnerability can be difficult, especially as a woman in a male-dominated field. I’ve had experiences where it reinforced colleagues’ biases, but it’s worth it to build the right culture. Allies who recognise this can be invaluable.
- Master new technologies whilst maintaining scientific rigour: It’s essential to keep up with AI and new tools, but never at the expense of fundamental scientific standards. Always ensure data quality, statistical validity, reproducibility, and ethical integrity.
- Prioritise ethical considerations and data integrity: As AI becomes more prominent in healthcare, understanding the ethical implications—such as algorithmic bias and data privacy—is vital. Develop knowledge in responsible AI and ensure innovation supports patient welfare.
- Stay adaptable whilst building foundational expertise: The field evolves quickly, so remain flexible and committed to lifelong learning. At the same time, develop strong foundations in statistics, biology, and computational methods. We can’t be experts in everything, but understanding the boundaries of each other’s contributions is key to effective collaboration.
What is the best piece of advice you have been given?
- Engage in at least one mentor/mentee relationship.
- Maintain perspective on rejection and criticism. Academic life involves constant evaluation—grant and paper rejections, job market challenges, and peer review. Build resilience by viewing feedback as data, not personal judgment. Every successful academic has faced numerous rejections; the key is to learn from it while maintaining confidence in your work.
- Master clear, concise communication. Whether writing emails, presenting ideas, or navigating difficult conversations, strong communication will determine much of your professional success. Tailor your message to your audience, get to the point quickly, and listen more than you speak. This can be especially tricky when coming from a different cultural background with different expectations.
Any advice you’d like to share?
- Build genuine relationships across disciplines. Professional success isn’t just about your research or technical ability—it’s also about collaboration, mentorship, and having a supportive network. Attend meetings outside your team, have coffee with colleagues, and join interdisciplinary initiatives. Be kind and think about how you can help others. These relationships often lead to unexpected opportunities and much-needed support during tough times.
- Continuously invest in learning new skills. Data disciplines evolve fast, and the skills that got you hired may not keep you relevant. Set aside time regularly to learn new tools, technologies, or methodologies. This doesn’t always mean formal education—online courses, workshops, or learning from colleagues can be just as valuable.
- Be adaptable and proactive during change. Change is constant—choose to engage with it. Being involved allows you to give feedback and better understand new processes and opportunities.
Why are you passionate about your career?
Biomedical data science is an extraordinarily fulfilling field. Working at the intersection of biology, medicine, and advanced analytics gives you the opportunity to turn large datasets into insights that directly impact patient outcomes and advance our understanding of health.
The interdisciplinary nature of the field is both its greatest challenge and greatest reward. You constantly collaborate with clinicians, biologists, statisticians, and computer scientists, learning new vocabularies and methods while bridging diverse perspectives to tackle complex problems.
This intellectual complexity demands ongoing growth, but synthesising knowledge across disciplines to answer questions that no single field could solve alone is deeply satisfying. For women entering this space, there’s also the added excitement of helping close the gender gap in computational sciences, bringing new perspectives to traditionally male-dominated fields, and serving as role models for the next generation of female data scientists.
More resources:
Article: Inspirational Leader of the Year award at the University of Liverpool Staff Awards and personal reflections on winning the award.
Article: Named in the North Innovation Women 2025
Article: Eva's Career Development in Research
Article: Celebrating University of Liverpool N8 New Pioneers 2018
Poster: Breaking Barriers, Building Bridges: Empowering Women in Data and Coding Careers
Podcast: Contribution to Treasure Island Pedagogies (episode 40)
Video: Research Seminar - Computational Biology for All
Widening Participation, Public Engagement and Outreach activities
Find out more about Eva on her University staff page and keep up to date with her on LinkedIn