Senior Principal Scientist - BioAI (h/f)

BioAI Senior Principal Scientist
Hybrid Working - 3 days on site, London
6 month rolling project, Inside IR35

emagine is a high-end professional services consultancy and solutions firm specialising in providing business and technology services to the financial services sector. We power progress, solve challenges, and deliver real results through tailored high-end consulting services and solutions.

We have created a culture of openness and integrity by building genuine and strong relationships and partnerships, enabling us to be uncompromising in our dedication to delivering the optimal service for our clients. Our commitment is not just towards our clients - we aim to foster a positive and equitable working environment with our consultants and colleagues, which stems from our core values: Confident, Dedicated, Responsible, Genuine.

As a Senior Principal Scientist (Computational Biology/AI), you will play a central role in advancing drug target discovery for cardiometabolic diseases (CMD) through rigorous computational modelling, large-scale data engineering, and applied machine learning. This role is rooted in hands-on computational science, where you will architect and implement scalable analytical pipelines, develop AI models, and translate complex biological datasets into predictive, decision-enabling outputs.

Key Responsibilities

• Lead the design and implementation of AI-driven analytical frameworks for drug target discovery.

• Develop, train, validate, and optimise machine learning and deep learning models on multimodal biological datasets.

• Apply advanced statistical learning, graph-based modelling, and network inference techniques to uncover causal biological mechanisms.

• Establish reproducible computational workflows and robust model evaluation pipelines.

• Perform large-scale data processing and feature engineering on ultra-high throughput in-vitro screening datasets.

• Analyse high-dimensional data (cell painting, transcriptomics, proteomics, metabolomics) using systems biology and computational modelling approaches.

• Build scalable data pipelines for structured and unstructured biological datasets.

• Apply dimensionality reduction, clustering, representation learning, and integrative multi-omics modelling techniques.

• Conduct rigorous benchmarking, validation, and sensitivity analyses to ensure model robustness and biological interpretability.

• Design scalable architectures for multimodal data integration across distributed compute environments.

• Optimise code for performance in high-performance computing (HPC) and cloud environments.

• Contribute to internal AI evaluation frameworks, including metrics for biological plausibility, predictive accuracy, and translational relevance.

• Implement version-controlled, containerised, and production-grade computational solutions.

• Translate complex computational findings into actionable biological hypotheses and drug target candidates.

• Partner with experimental scientists to iteratively refine computational models using wet-lab feedback.

• Influence systems biology strategy by defining computational standards, modelling approaches, and AI governance practices.

• Communicate complex technical insights clearly to scientific, technical, and executive stakeholders.

Required Qualifications & Experience

• Master's degree or PhD in Computer Science, Computational Biology, Bioinformatics, Machine Learning, Systems Biology, or a related quantitative discipline.

• Strong hands-on experience in:

  • Python (NumPy, pandas, PyTorch, TensorFlow, scikit-learn) and/or R

  • Machine learning and deep learning for structured and high-dimensional data

  • Statistical modelling and causal inference

  • Network biology and graph-based learning approaches

• Experience building scalable data processing pipelines in cloud or HPC environments.

• Proven ability to independently design, implement, and validate computational models in production or research settings.

• Experience integrating multimodal datasets and handling noisy, incomplete biological data.

• Significant experience in pharma, biotech, or tech-bio environments.

• Demonstrated impact in computational drug target identification or translational AI initiatives.

• Experience working in interdisciplinary teams bridging computational and experimental domains.

• Experience applying AI to cardiometabolic disease datasets.

• Background in cellular or molecular biology sufficient to contextualise computational outputs biologically.

• Experience developing interpretable AI frameworks for biomedical decision-making.

emagine is an equal opportunity employer, and employment practices are based strictly on merit. It is the policy of the Company to give equal opportunity in employment regardless of sex, sexual orientation, marital status, race, age, disability, gender reassignment, pregnancy and maternity, religion or ethnic origin