Principal/Senior Data Scientist

Salary Per Annum: £53,717 – £63,815

You will lead and contribute to transformative projects that integrate single-cell genomics, spatial transcriptomics, and generative AI to build next-generation models for understanding tissue biology and cellular dynamics across organs such as the pancreas, kidney, skin, and liver.

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• Spatial & Multi-omics Atlas Construction: Build large-scale spatial and single-cell atlases across diseased tissues (pancreas, kidney, skin, liver) using spatial transcriptomics, scRNA-seq, and multiome data in collaboration with leading Sanger groups.
  
• Generative AI for Cell Fate & Perturbations: Develop diffusion, flow-matching, and transformer-based generative models to predict cell fate, tissue remodelling, and drug or perturbation responses in silico.
  
• Foundational Models for Single-Cell Biology: Train large, generalizable deep models across public and internal datasets to support the Human Cell Atlas and broad Sanger research programs.
  
• Open Targets Translational AI Projects: Apply foundational and multi-omics models to real-world challenges in drug discovery, target identification, and target safety in collaboration with major pharma partners.
  
• Agentic AI for Scientific Reasoning & Experiment Design: Develop AI agents capable of hypothesis generation, experiment planning, and multi-step scientific workflows using reinforcement learning and tool-use models.
  
• Core Machine Learning Research: Advance fundamental ML methods—including advanced generative modelling, scalable training algorithms, representation learning, and uncertainty modelling—tailored for biological data.
  
• Multimodal Learning (Imaging + Genomics + Clinical Data): Create models that integrate histopathology imaging, spatial proteomics, single-cell genomics, and patient-level clinical data to learn unified biological and clinical representations.
  
• Leap Project: Develop large-scale AI models to stratify patients using diverse multi-omics data, with a strong commitment to equity and inclusion, particularly in women’s health, in collaboration with Roser Vento-Tormo at the Sanger Institute.
  

Join the Lotfollahi Group, an interdisciplinary team of ML researchers, computational biologists, clinicians and experimentalists working with the Human Cell Atlas and international leaders.

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• Akbar Nejat et al., Mapping and reprogramming human tissue microenvironments with MintFlow (bioRxiv, 2025)
  
• Birk et al., Quantitative characterization of cell niches in spatially resolved omics data, Nature Genetics (2025)
  
• Jeong et al., SIGMMA: Hierarchical Graph-Based Multi-Scale Multi-modal Contrastive Alignment of Histopathology Image and Spatial Transcriptome (arXiv, 2025)
  
• Sanian et al., 3D-Guided Scalable Flow Matching for Generating Volumetric Tissue Spatial Transcriptomics from Serial Histology (arXiv, 2025)
  

We welcome applications from diverse backgrounds passionate about biology, foundation model development, modelling cellular perturbation responses, predicting patient behaviours and analysing multi-modal biological data.

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• MSc and/or Ph.D. in a relevant quantitative discipline (e.g., Computer Science, Computational Biology, Genetics, Bioinformatics, Physics, Engineering, or Applied Statistics/Mathematics).
  
• Proven experience using advanced statistical techniques, machine learning, and modern deep learning techniques.
  
• Previous ML work experience in a scientific/academic environment (RA/Internships are considered as work experience).
  
• Strong knowledge of Python, including core data science libraries such as Scikit-Learn, SciPy, TensorFlow, and PyTorch.
  
• Knowledge of software development best practices and collaboration tools, including git-based version control, python package management, and code reviews.
  
• Excellent communication skills, with the ability to explain complex machine learning algorithms and statistical methods to non-technical stakeholders.
  
• Experience working with cloud environments and tools, such as Amazon AWS S3, EC2, etc.
  
• Evidence of related work experience as a researcher in the area of Machine learning.
  
• Strong publication record.
  
• Ability to quickly understand scientific, technical, and process challenges and breakdown complex problems into actionable steps.
  
• Ability to work in a frequently changing environment with the capability to interpret management information to amend plans.
  
• Ability to prioritize, manage workload, and deliver agreed activities consistently on time.
  
• Demonstrate good networking, influencing and relationship building skills.
  
• Strategic thinking is the ability to see the ‘bigger picture.
  
• Ability to build collaborative working relationships with internal and external stakeholders at all levels.
  
• Demonstrates inclusivity and respect for all.
  

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• Experience in supervision (PhD students and Postdoctoral Fellows).
  
• Experience in writing manuscripts for publication.
  
• Experience working with cloud environments and tools, such as Amazon AWS S3, EC2, etc.
  
• Relevant solid publication record in either machine learning or application of machine learning in biology.
  

Please submit your CV and a cover letter detailing your research experience, interest in the focus area(s), and future aspirations. Closing Date: 8th February 2026.

We recognize hybrid working benefits, including an improved work-life balance and the ability to organise working time so that collaborative opportunities and team discussions are facilitated on campus. The hybrid working arrangement will vary for different roles and teams. The nature of your role and the type of work you do will determine if a hybrid working arrangement is possible.

We aim to attract, recruit, retain and develop talent from the widest possible talent pool, thereby gaining insight and access to different markets to generate a greater impact on the world. We have a supportive culture with staff networks, LGBTQ+, Parents and Carers, Disability and Race Equity to bring people together to share experiences, offer specific support and development opportunities and raise awareness. We will consider all individuals without discrimination and are committed to creating an inclusive environment for all employees, where everyone can thrive.

We are proud to deliver an awarding campus-wide employee wellbeing strategy and programme. The importance of good health and adopting a healthier lifestyle and the commitment to reduce work-related stress is strongly acknowledged and recognised at Sanger Institute. Sanger Institute became a signatory of the International Technician Commitment initiative. The Technician Commitment aims to empower and ensure visibility, recognition, career development and sustainability for technicians working in higher education and research, across all disciplines.