We are seeking a Machine Learning Scientist / Engineer to develop and deploy data-driven models that improve biomanufacturing processes for recombinant proteins, mRNA, cell and gene therapies. The role focuses on applying ML to upstream and downstream process data, analytical datasets, and manufacturing systems to enhance yield, quality, robustness, and process understanding of therapeutics.
Key Responsibilities
Machine learning and data analytics
- Develop ML models for process optimisation, prediction, and control , including yield, CQAs, aggregation, glycosylation, and impurity profiles.
- Apply supervised, unsupervised, and multivariate methods (e.g. neural networks, regression, classification, clustering, PCA / PLS, Bayesian models).
- Integrate omics, analytical (LC-MS, spectroscopy), and process data into unified modelling frameworks.
- Perform feature engineering informed by bioprocess and product knowledge .
Biomanufacturing applications
Digitially Improve AI driven bioprocessesDevelop soft sensors and digital twins for real-time or near-real-time process monitoring.Replace or augment design of experiments (DOE) with ML-driven process optimisation .Deployment and validation
Translate models into production-ready tools (Python / R, APIs, dashboards).Perform model validation, versioning, and lifecycle management.Collaborative environment
Work closely with process scientists, analytical scientists, engineers, and quality teams .Clearly communicate model assumptions, limitations, and impact to non-ML stakeholders.Required qualifications
BSc or MSc in Machine Learning, Data Science, Chemical / Biochemical Engineering, Bioinformatics, Systems Biology , or related field.Strong programming skills in Python (e.g. scikit-learn, PyTorch, TensorFlow)Solid understanding of statistics, experimental design, and model validation .Key competencies
Modelling mindsetStrong problem-solving and analytical skillsExcellent communicationWhat we offer :
Opportunity to apply ML to real-world, high-impact manufacturing challengesCollaborative environment at the interface of AI, biology, and engineeringExposure to cutting-edge therapeutics and advanced manufacturing platforms