As we confront new global challenges and pursue transformative innovations in biotechnology and beyond, the skills required of tomorrow’s scientists are shifting.
To explore this evolution, we asked a simple question to experts from across science academia and industry: What new skills or roles do you think scientists will need in the next decade to stay effective in an evolving research landscape?
The collective responses of leading voices in science and biotech provide guidance not only for aspiring researchers but also for institutions, employers and mentors who seek to support and shape the future of science.
Dr. Stephen Hilton, associate professor at the UCL School of Pharmacy
The next decade will demand that scientists become not only subject matter experts but also digitally fluent collaborators. As virtual reality (VR), artificial intelligence (AI), automation and global connectivity reshape the research landscape, several key skillsets and roles will become essential:
- Digital lab fluency: Scientists will need to understand and operate in virtual environments – navigating digital twins of lab spaces, managing remote experiments and working alongside AI avatars and robotic systems.
- Data-driven thinking: With real-time telemetry, cloud-integrated workflows and AI analysis becoming standard, researchers must be comfortable with big data, machine learning basics and automated decision-making tools.
- Multidisciplinary agility: Future labs won’t operate in silos. Chemists, AI specialists and VR developers will collaborate daily. Scientists who can bridge disciplines, or at least communicate fluently across them, will be invaluable.
- Global communication: Multilingualism and cross-cultural collaboration skills will rise in importance.
- Ethics and responsible innovation: As AI plays a greater role in experimentation and discovery, understanding the ethical implications of autonomous systems and responsible data use will be critical.
We also expect to see entirely new roles, such as VR lab coordinator, AI model trainer for science or telemetry integration specialist, emerge within research teams. Ultimately, adaptability, curiosity and a willingness to learn new tools will be just as important as deep expertise.
Dr. Lindsay Davies, CSO at NextCell Pharma AB
Transferable skill sets have always been and continue to be crucial to roles within research, whether in industry or academia. Research fields evolve so quickly that it is not possible to specialize in a defined niche without risk. Therefore, researchers are thinking broader, widening their experiences and applying knowledge across disciplines and fields.
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Alexander Seyf, CEO at Autolomous
The future demands a “T-shaped” scientist: Deep scientific expertise augmented by broad proficiency in data science and digital tools, regulatory intelligence and digital compliance, as well as collaborative skills.
One critical skill gap that must be addressed is the lack of expertise in health economics and enterprise leadership within the life sciences sector.
Building a successful, scalable organization requires more than scientific excellence – it demands a deep understanding of value-based healthcare, market dynamics and sustainable business models.
To bridge this gap, we must not only invest in upskilling but also proactively bring in capabilities from other industries, cross-fertilizing knowledge and proven leadership practices. This multidisciplinary integration is essential to drive innovation, ensure long-term viability and maximize impact across the healthcare ecosystem.
Fraser McLeod, vice president, general manager QA/QC at Waters
The capabilities and limitations of AI are becoming increasingly important to understand. It’s essential to recognize not only what AI can do, but also the quality of data it requires and the underlying assumptions it relies on to use this powerful tool responsibly.
Professor Samra Turajlic, PhD, group leader and consultant medical oncologist at The Francis Crick Institute and The Royal Marsden Hospital
We’re moving into a research era defined by complexity, integration and scale. Scientists will increasingly need to operate at the intersection of biology, data science and systems thinking. That means deep technical skills in areas like multiomic data analysis, machine learning and spatial biology, but also the ability to collaborate effectively across disciplines and sectors.
Importantly, we need new roles for field experts such as bioinformaticians and clinicians who are embedded in academic teams and valued as career scientists, not just as support.
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Lori Ball, CEO, Astoriom
Beyond traditional scientific expertise, future scientists will need proficiency in data science, digital compliance tools and sustainability assessment. Skills in AI, automation and laboratory informatics will be essential for navigating complex data environments.
We’re also seeing the rise of hybrid roles – data compliance officers, sample logistics managers and sustainability champions – within research and development teams. These functions ensure that scientific innovation progresses within the frameworks of global regulation, digital transformation and environmental responsibility.
Madusha Peiris, PhD, founder and CEO, Elcella
In the next decade, scientists should aim to develop an interdisciplinary skill set to support innovation and develop truly innovative solutions. Specifically, data science and AI literacy will be critical, not just for analyzing complex datasets, but for designing experiments, generating hypotheses and accelerating discovery. Scientists can gain important insights and solutions by being systems thinkers capable of integrating insights across biological pathways. Indeed, increasing pre-clinical scientific data from human-centric, whole-tissue models is an underutilized yet critical strategy for effective solutions.
Benjamin Lilienfeld, PhD, lifecycle leader for the Serum Work Area systems, Roche Diagnostics
Beyond traditional expertise, the next decade will emphasize proficiency in data science and computational tools, enabling the analysis of increasingly complex datasets. Interdisciplinary collaboration and effective communication will be crucial for tackling multifaceted research questions.
In addition, to remain effective in the rapidly evolving research landscape of the next decade, scientists will need to adapt to emerging roles driven by the integration of AI solutions into digital knowledge. This includes developing a strong foundational literacy in AI and machine learning, encompassing the ability to critically evaluate AI outputs and effectively utilize prompt engineering with large language models.
Communication skills will also evolve, requiring seamless interaction with AI specialists and the ability to articulate AI-driven findings to diverse audiences. Proficiency in computational tools, including programming languages and cloud computing platforms, will become increasingly essential.
Tomek Czernuszewicz, PhD, director of Ultrasound Imaging, Revvity
The next generation of scientists will likely have to become experts at balancing deep expertise in their field with rapid adaptability to new tools and collaborative approaches.
Budgets seem to be getting increasingly smaller, and scientists and engineers will need to constantly “do more with less.” That means piggybacking work with collaborators, forming strategic cross-disciplinary partnerships, and becoming more efficient with resources.
For example, instead of collecting one biomarker per mouse, collect hundreds of biomarkers with new in vivo and ex vivo tools and use AI approaches to manage datasets that otherwise would be too complex for traditional methods. Lastly, new scientists will need to develop exceedingly effective communication skills to articulate the value and potential impact of their research to secure funding in an increasingly competitive landscape (particularly as AI-assisted writing tools become more prevalent and sophisticated, elevating the level of all grants).
