Do you want to help us improve human health and understand life on Earth? Make your mark by shaping the future to enable or deliver life-changing science to solve some of humanity's greatest challenges.

Principal Research Data Scientist

We seek a Principal Machine Learning Research Data Scientist Scientist to join a collaborative project between the Wellcome Sanger Institute and Open Targets (targets (https://#removed#/). This project aims to leverage datasets internally generated at the Sanger Institute and publicly available data from human cells to create foundational models for biology, enhancing our understanding of life's rules and improving health for all. You will work within an interdisciplinary team of life scientists and computer/ML scientists, with a shared objective of advancing biological research through these foundational models. This role will sit within the AI/ML Faculty group led by Dr. Mohammad Lotfollahi, and the successful candidates, across different seniority levels (senior and principal), will be responsible for delivering their portfolio of scientific research projects as part of the broader team strategy.

About the Role

Your role will involve designing foundational models leveraging multi-modal readouts. This includes integrating and processing data from various sources to develop robust and versatile AI models. To achieve this, you will work with open-source software, proposing, developing, and maintaining new solutions to analyze and interpret large-scale single-cell datasets. We have access to unique data and are also in the position to generate data to train unique models. Additionally, we have substantial computational power and GPU resources to train large models efficiently.

Our teams are well-positioned to tackle this problem with experience in both generating and analyzing datasets, including millions of cells across multiple tissues and conditions (e.g., disease, healthy). This involves a detailed understanding of the training of large-scale ML models and a track record of undertaking large data-science projects.

You will be responsible for:

• Independently manage and lead machine learning research projects and write outcomes in a scientific publication for submission to journals or machine learning conferences (ICLR, ICML, CVPR, etc).
• Collaborate with team members, propose, develop, and evaluate new machine learning models that enable understanding single-cell data and its application in drug discovery.
• Work with Ph.D. students and postdocs in collaborating teams on developing solutions for interdisciplinary scientific problems in biology, providing supervision and training to junior members of the team.
• Contribute to writing scientific papers on biotechnology and biology.
• Distill your developed solutions into open-source and easy-to-install packages with documentation that facilitates the usage of your solution for downstream users, including biologists and bioinformaticians.
• Present your research and analysis pipelines to internal and external audiences.

About You:

You will be supported in your personal and professional development and have the opportunity to lead peer-reviewed publications around using genetics and genomics approaches to guide drug discovery and present them at national and international conferences.

Essential Skills:

• Ph.D. or M.Sc. with equivalent research experience in a relevant quantitative discipline (e.g., Computer Science, Computational Biology, Genetics, Bioinformatics, Physics, Engineering, or Applied Statistics/Mathematics)

• Previous ML work experience in 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.

• Expertise in machine learning algorithms and frameworks, with experience in designing, training, and deploying ML models.

• Proficiency in handling and processing large datasets, including techniques for data cleaning, feature engineering, and data augmentation.

• Experience with high-performance computing environments, including the use of GPUs for training large-scale machine learning models.

• Experience in natural language processing (NLP) and training models based on transformer architectures, such as BERT and GPT.

• Familiarity with generative models such as diffusion models and flow matching.

• Knowledge of software development good practices and collaboration tools, including git-based version control, Python package management, and code reviews.

• Strong problem-solving skills with the ability to analyze complex data and derive actionable insights.

• Excellent communication skills, with the ability to explain complex machine learning algorithms and statistical methods to non-technical stakeholders.

• Evidence of related work experience as a researcher in the area of Machine learning
• Strong publication record, first author position ideal

In addition to the above technical skills, you will also have the following:

• 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

Relevant publication of the groups:

• Lotfollahi, M., Naghipourfar, M., Luecken, M. D., Khajavi, M., Büttner, M., Wagenstetter, M., Avsec, Ž., Gayoso, A., Yosef, N., Interlandi, M. & Others. Mapping single-cell data to reference atlases by transfer learning. Nature Biotechnology 1-10 (2021).
• Lotfollahi, M., Wolf, F. A. & Theis, F. J. scGen predicts single-cell perturbation responses. Nature Methods 16, #removed# (2019).
• Lotfollahi, M., Rybakov, S., Hrovatin, K., Hediyeh-Zadeh, S., Talavera-López, C., Misharin, A. V. & Theis, F. J. Biologically informed deep learning to query gene programs in single cell atlases. Nature Cell Biology (2023).

Other Information:

Please upload your a cover letter along with your current CV and complete our short application form to apply.

Closing date: 3rd July 2024
Proposed Interview Date: 10 July 2024

Contract type: 3 year fixed term

Salary per annum: £53,717 - £64,459 (dependent on skills and experience)

Hybrid Working at Wellcome Sanger:

We recognise that there are many benefits to Hybrid Working; including an improved work-life balance, with more focused time, as well as 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.

Equality, Diversity and Inclusion:

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 the following 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. The networks are also a place for allies to provide support to others.

We want our people to be whoever they want to be because we believe people who bring their best selves to work, do their best work. That's why we're committed to creating a truly inclusive culture at Sanger Institute. We will consider all individuals without discrimination and are committed to creating an inclusive environment for all employees, where everyone can thrive.

Our Benefits:

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 In March 2018. 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.