Senior Translational Scientist

We were engaged to recruit a Senior Translational Scientist for a client that was building a foundation model for biology to help researchers accelerate scientific discovery.

The brief centred on connecting AI predictions with experimental biology. Reporting to the Chief Scientific Officer, the scientist would translate model outputs into testable hypotheses, design validation programmes and generate biological insights to support model development and partner applications.

The role

Working closely with ML researchers, computational biologists and experimental partners, the Senior Translational Scientist would:

• Translate model predictions into scientifically robust, experimentally testable hypotheses.
• Design and oversee computational and wet-lab validation programmes to confirm or challenge predictions.
• Feed structured experimental findings back to the ML team to improve biological accuracy.
• Identify applications across areas such as target discovery and patient stratification.
• Demonstrate the model’s scientific value through publications and public demonstrations.
• Develop repeatable processes for turning computational predictions into experiments.

The position offered access to dedicated GPU infrastructure, public and proprietary multi-omics datasets, and funding for external wet-lab collaborations.

The profile we were asked to identify

The search focused on scientists with a PhD in molecular biology, genetics, systems biology or a related discipline, alongside at least five years’ experience translating computational predictions into experimental programmes.

Key requirements included:

• A track record of published or patented results.
• Expertise in distinguishing genuine biological signals from model artefacts.
• A working understanding of modern ML approaches, including transformers, embeddings and uncertainty quantification.
• Evidence of progressing a computational finding into a wet-lab or clinical collaboration.
• The ability to shape scientific direction in an early-stage, fast-moving environment.

What made the opportunity distinctive

This Paris-based, hybrid role offered the opportunity to influence both scientific and product development within a growing interdisciplinary team. The central challenge was to establish how a foundation model for biology could deliver practical value across research applications, with experimental findings directly informing future model development.

Building a team at the intersection of AI and biology? Contact our team to discuss your hiring plans.