What you will be doing: Leading on developing and implementing machine learning algorithms and models to analyse large datasets and derive meaningful insights for crop trait development. Collaborating with cross-functional teams to integrate machine learning capabilities into our existing trait discovery workflows and pipelines. Solving complex problems, gaining input from key multidisciplinary stakeholders and coming to the best decision, often where there might be more than one solution. Staying up to date with the latest advancements in machine learning and bioinformatics tools, databases, and software packages; making recommendations to the wider team about how to transition the best into our ways of working. Communicating research questions, methodologies, and results to both expert and non-expert stakeholders effectively. Providing bioinformatics expertise to those around you, providing a coaching and mentoring approach where appropriate. What qualities you bring to the table: PhD (or Masters) in computational biology, bioinformatics, data science, or a related field where data science and applied statistics are applied to large datasets. Applicants with evidence of equivalent work experience will also be considered. Proficiency in programming languages, with strong expertise in Python and/or R. In-depth theoretical knowledge of the latest methods and applications for machine learning approaches. Strong familiarity with bioinformatics tools, databases, and software packages. Proficiency in statistical and machine learning packages in Python and/or R (e.g., Scikit-learn, TensorFlow, PyTorch, Caret). Strong problem-solving skills and ability to work collaboratively in a team environment. Excellent communication skills for effectively conveying research questions and results. Nice to have: 3 years of experience in academia or industry using applied statistics and machine learning tools for multiomic dataset analysis. Demonstrable contributions to the field using machine learning through publications or GitHub projects and a proven track record of technical leadership on RD projects. Knowledge of graph machine learning methods, including graph neural networks. Knowledge of linear algebra and calculus for data science and machine learning. Experience working in an early-stage start-up. Experience working in plant science. Please apply on the Lifelancer platform at the below link for screening steps & quicker response. https://lifelancer.com/jobs/view/648247ce4eb20b72a807b46821582f78