Trait Design Scientist Wild Bioscience Ltd - Design Team

The role

We are looking for a motivated and skilled Trait Design Scientist, ideally with a background in photosynthesis research, to drive our R&D initiatives. At Wild, we are building proprietary and "new-to-science" datasets that we use to discover new yield traits. You will play a key role in analysing these datasets and driving the future direction of data collection to accelerate this discovery work.

Location

We're headquartered in Milton Park, a business and technology park in Oxfordshire. This is an on-site role, though we offer flexibility and hybrid working when possible.

Job requirements

• A PhD in a relevant discipline, e.g. Plant Science, Photosynthesis, Plant Physiology, Bioinformatics.
• Proven experience delivering complex scientific research projects and driving their strategic direction.
• Deep theoretical and practical understanding of photosynthesis; you are comfortable interpreting complex physiological data to generate biological hypotheses.
• Experience using gas exchange and chlorophyll fluorescence systems (e.g. LICORs, Walz), and/or other photosynthetic mechanistic studies used to dissect photosynthetic performance.
• A strong understanding of the genetic mechanisms that govern plant phenotypes and the factors that influence gene expression.
• Familiarity with Linux, bioinformatic tools, and the application of machine learning to genomic/phenotype data is highly desirable.
• A growth-oriented mindset focused on continuous improvement - evaluating new tools and sparking innovation within the team.
• Ability to distil complex data into clear hypothesis and lead collaborative decision-making sessions.

Job responsibilities

• Lead the design and management of experiments to collect bespoke physiology and genetic datasets across diverse species.
• Establish robust phenotyping methods and train junior team members to ensure high-quality data collection at scale.
• Utilise machine learning and bioinformatic tools to mine proprietary datasets, identifying causal genetics behind key photosynthesis traits.
• Critically evaluate machine learning / AI outputs by integrating photosynthesis expertise with literature reviews and data from our proprietary evolutionary and cell-based platform.
• Lead trait design sessions, collaborating with other photosynthesis experts, construct engineers, and bioinformaticians to inform phase promotion decisions.
• Iteratively improve data collection protocols based on the latest photosynthesis research.