Senior Investigator Scientist LMS 2755
Salary: £52,253 £55,830 plus London allowances £5,560 per annum
Band (Grade): MRC 3 | Fixed term for 3 years | Full time | Closing date: 28 August 2026
The Genomics of Obesity Group:
Headed by PI Dr William Scott, the LMS is appointing a Senior Investigator Scientist with an established track record of genomic data science, including advanced single-cell and multimodal data analysis, to perform semi-independent research, lead on the methodological and analytical developments in the lab, and support and co-supervise junior lab members.
The main key responsibilities of the role include:
Lead the computational analysis of large-scale single-cell, spatial and multimodal -omics datasets from human adipose tissue, with a high degree of independence.
Develop, benchmark and apply advanced statistical and machine-learning methods for single-cell and multimodal data integration (e.g. transfer learning, latent-variable models, in silico perturbation modelling).
Lead the construction, curation and analysis of human adipose tissue single cell datasets including published datasets and cohorts.
Apply statistical genomics approaches to link cellular and molecular phenotypes to clinical traits and genetic variation.
Design and implement reproducible, scalable analysis pipelines and workflows for high-dimensional genomics data.
Provide effective supervision to undergraduate student(s) hosted in the lab working on relevant computational projects.
Train, co-supervise and assist lab members with their analyses, particularly those involving complex or novel computational methods.
Lead on data management, computational infrastructure and analytical good-practice (version control, reproducibility) within the lab.
Participate and, where required, project-manage collaborations — including clinical cohorts, experimental teams, methods and machine-learning groups, and LMS core facilities — to expand the scope of the lab’s research.
Write-up and publish research results, and assist in preparing collaborative work for publication.
Present at scientific meetings.
Contribute to writing bids for research grants.
Balance independent work with team science, both within the lab, other teams in the institute and beyond.
Take an active part in the academic activities of the group including group meetings, group journal clubs, training and organisational aspects.
Take an active part in the academic activities of the LMS, including seminar presentations.
Support the LMS’s values of Equality, Diversity and Inclusion.
Equality & Diversity
The MRC values the diverse skills and experience of its employees and is committed to achieving equality of treatment for all.
Our objectives are that all individuals shall have access to equal opportunities for employment and advancement on the basis of their skills, aptitudes and abilities.
The MRC is committed to the engagement and retention of the best possible talent and to creating an environment that encourages excellence in scientific research through good equalities and diversity leadership and management.
All role holders are responsible for supporting LMS EDI initiatives and development in management practices.
Corporate/Local responsibilities & requirements
The job holder must at all times carry out their responsibilities with due regard to the MRC’s:
• Code of Conduct
• Equality and Diversity policy
• Health and Safety policy
• Data Protection and Security policy
Job descriptions should be reviewed on a regular basis and at the annual appraisal. Any changes should be made and agreed between the post holder and their manager.
The above lists are not exhaustive and the job holder is required to undertake such duties as may reasonably be requested within the scope of the post. All employees are required to act professionally, co-operatively and flexibly in line with the requirements of the post and the MRC.
Person requirements
Education / Qualifications / Training required (will be assessed from application form):
Essential:
• A PhD supported by extensive post-doctoral experience in either industry or academia in computational biology, statistical genomics, bioinformatics, machine learning, statistics or a related quantitative discipline.
Previous work experience (will be assessed from application form and at interview):
Essential:
• Experience of the computational analysis of single-cell and/or multimodal -omics data, demonstrated by excellent (joint-)lead author publications. Note: preprints will be considered, but at least one peer-reviewed publication is expected.
• Direct experience of multimodal data analysis in adipose and other metabolic tissues.
• Experience with Image analysis software.
• Extended the requirements to include experience in analysing both Human and Mouse datasets.
• Spatial genomic expertise applied to adipose/other metabolic tissues.
• Experience of developing, benchmarking and applying analytical methods or pipelines.
• A record of scientific originality and creativity, demonstrated through publications.
• Experience of working with large, high-dimensional biological datasets.
• Experience of supporting and supervising students and junior researchers.
Desirable:
• Experience as a senior, semi-independent researcher in academia or industry, such as a senior postdoc, career development fellow or team leader.
• Experience of leading data management or computational infrastructure.
• Experience working in collaborative environments, including international consortia.
• Familiarity with cellular and animal models, to assist with experimental design required to expand on observations obtained from omics data.
• Experience of working with clinical / patient-derived data and longitudinal cohorts.
Knowledge and experience (will be assessed from application form and at interview):
Essential:
• Excellent programming skills (e.g. Python and/or R) for the analysis of genomics data.
• Strong understanding of advanced single-cell genomics data analysis (e.g. scRNA-seq, single-cell multiome).
• A working knowledge of spatial transcriptomics analysis (e.g. 10X Xenium) and multimodal data integration.
• A solid grounding in statistics and statistical genomics.
• A good understanding of NGS data analysis.
Desirable:
• Experience with machine-learning / deep-learning frameworks for single-cell or spatial data (e.g. transfer learning, latent-variable or generative models).
• Familiarity with reproducible-research practices (version control, workflow managers, high-performance or cloud computing).
Personal skills / Behaviours / Qualities (will be assessed at the interview):
Essential:
Good leadership skills that inspire and motivate others to support high quality outputs.
Excellent communication skills to convey complex information clearly to wide audiences.
Ability to organise and prioritise workload, and to delegate appropriate tasks to team members ensuring deadlines are met.
Commitment to maintaining confidentiality of data at all times.
A flexible approach to working, with the ability to sometimes work outside of core hours (including weekends) if the requirements of the project demand.
A high level of motivation to learn new concepts and acquire new skills in biology and computing, and to seek novel and creative solutions to scientific problems.
Application Process:
Interested candidates are encouraged to upload their up-to-date CV and personal statement, outlining their research achievements and relevant experience, and details of at least 2 references.
Please note that applications may be reviewed by both LMS and Imperial staff
