Principal Research Scientist

Immunocore (NASDAQ: IMCR) is a pioneering, commercial-stage T cell receptor biotechnology company whose purpose is to develop and commercialize a new generation of transformative medicines which address unmet patient needs in oncology, infectious diseases and autoimmune disease. Our leaders in R&D are internationally recognised as some of the biotech industry's most successful drug developers. We are creating not just an environment where great minds can interact but an innovation powerhouse answering the big questions.

Focused on delivering first-in-class biological therapies to patients, we have developed a highly innovative soluble TCR platform. Our ImmTAX molecules underpin a new generation of precision engineered drugs that harness the immune system to treat a broad spectrum of diseases with high unmet medical need, including oncology, infectious diseases and autoimmune diseases.

We strive to create a diverse and inclusive workplace, while seeking talented individuals to work with us across the many functions that will allow us to deliver new medicines to transform the lives of patients. You will work with outstanding people who together pioneer the research, development and commercialization of bi-specific TCR therapies. We aim to create an environment where individual contributions and initiatives can be maximized, while fostering a culture of collaboration, based on respect and integrity. We want each individual employee to own their career, as part of high-performing teams, and in the context of on-the-job and formal continuous development and training, as well as constructive feedback. We always strive to identify ways to improve what we do and how we do it, by asking questions, voicing opinions, exploring various approaches and staying connected with healthcare professionals, patients, academia and other key partners.

The Principal Scientist In Silico Biologics Design will provide world class expertise in machine learning to drive the establishment of robust in silico methodologies that integrate computational predictions with empirical laboratory data to revolutionise the design of our class-leading T-cell receptor (TCR) drugs. Using our wealth of structural data, combined with further in-house and public domain data sets, the successful applicant will develop ML algorithms to impact the design of future drugs. Working closely with the In Silico and automation teams within Protein Engineering, you will provide scientific and technical leadership relating to machine learning that will ultimately result in further progression of our growing pipeline of first-in-class drugs to benefit patients.

To develop and assess advanced machine learning (ML) models for novel antibody and TCR drug candidate optimisation and, looking to impact de novo drug design in the future:

• Utilise our extensive in-house structural and specificity data sets to extract further value
• Work with colleagues across Research to identify training data from the public domain and help shape the development of our in-house data sets to support the optimisation of ML algorithms
• Drive the establishment of virtual screening tools including:
• Assist with TCR library design for multifactorial lead optimisation
• Work with interdisciplinary teams across structural biology, next-generation sequencing (NGS), and bioinformatics and data science to integrate machine learning into TCR discovery and optimisation.
• Promote utilisation of ML algorithms among colleagues and mentor and train others in their deployment
• Present research outcomes at all levels, including Protein Engineering lab meetings, Research leadership team meetings, other governance meetings and at external conferences

Demonstrated ability to build and evaluate machine learning models:

• At least 8 years of experience in antibody, TCR or biologics design, evidenced by a strong publication record.
• Advanced Python programming skills, focusing on data science and machine learning.
• Managed sizeable research projects, understanding the wider implications of project outcomes and strategic objectives.
• Demonstrates effective coaching skills and brings vision and strategy to life for others.
• Demonstrated ability to build and sustain networks and external collaboration with scientific leaders in the field.

Expertise in structural biology and modelling:

• Experience in NGS analysis.
• Experience with biologics data management software including Genedata and in handling and integrating large-scale biological datasets from different sources.
• AWS
• PhD in Computational Biology, Computational Chemistry, Bioinformatics, or related discipline.