At GSK, we have bold ambitions for patients, aiming to positively impact the health of 2.5 billion people by the end of the decade. R&D is committed to discovering and delivering transformational vaccines and medicines to prevent and change the course of disease. Science and technology are coming together in a way they never have before, and we have strong tech-enabled capabilities that allow us to build a deeper understanding of the patient, human biology and disease mechanisms, and transform medical discovery.  We are revolutionising the way we do R&D. We’re uniting science, technology and talent to get ahead of disease together.

Job Purpose:

• The new Research Technologies Data and Predictive Sciences (DaPS) function is focused on harnessing the power of GSK’s data-as-an-asset to drive research productivity upper quartile ambitions.
• As an Investigator in the Cheminformatics (CIX) group, you will be focused on developing, integrating and embedding advanced computational methods and predictive in silico models that accelerate discovery of medicines.   
• DaPS will only be successful by working in close collaboration with Research Technologies, Biology Units, GSK Tech, AIML, Vaccines, Risk & Compliance, and across lines within the function itself, and by developing and fostering a high-performing team culture of collaboration, curiosity, consistency, agility, quality, peer review, and continuous improvement with a relentless focus on enabling value realization through end-user uptake and measuring impact.

Key Accountabilities / Responsibilities:

• You will work with other DaPS team members as well as the modelling groups in other parts of Research Tech (focus on Molecular Modalities and Pre-Clinical Sciences) to develop or embed methods that are applicable to our growing modality toolbox including covalent binders, PROTACS and ADCs, and oligos.
• You will work closely with program teams and modelling teams in Research Technologies to solve drug discovery project issues using cheminformatics tools and solutions or other approaches relevant for the problem at hand (ML, Statistics, comp chem, comp bio, bioinformatics, etc.).
• You will be a key contributor to the development of novel methods and solutions, contributing code & algorithms to the department’s scientific infrastructure and automated modelling/design platforms.
• You will partner with the Tech organizations to facilitate the move to cloud.
• You will prepare and present results of key validation experiments, details of capability builds, and developments on active drug discovery projects to internal and external groups in a way that is both informative and accessible to the non-subject matter expert.
• You will work with others within a multidisciplinary matrix team that spans different organizations to execute joint objectives.

Basic Qualifications:

•    PhD in Computational Chemistry or Life Sciences or equivalent or up to three years of  pharmaceutical or biotech experience.

•    Three or more years of experience in computational sciences which will include a deep knowledge of cheminformatics and machine learning and related methods such as 3D structure and ligand based design, virtual screening, generative design, QSAR, active learning, etc.

Preferred Qualifications:

• A track record of impactful publications.
• Track record of leading teams, matrix groups or method development projects.
• Demonstrated track record in leading change.

• Demonstrated ability to work in a matrixed team environment.
• Demonstrated ability to build robust statistical models from large scientific data sets.
• Track record of developing robust scientific software in languages such as C/C++, Python or Java.
• Experience with modern code development/management environments and coding standards.
• Evidence of identifying, developing, and applying innovative solutions to scientific and technological problems faced in the Life Sciences
• Deep expertise to programmatically collect, combine, mine and analyze complex biological and chemical data
• Deep expertise with chemical toolkits such as ChemAxon, OEChem, or RDkit
• Deep expertise with scientific pipelining tools such as Pipeline Pilot or Knime.
• Evidence of strong critical thinking skills.
• Evidence of strong problem-solving & high learning agility
• Excellent written and oral communication skills and the ability to interact effectively with scientists in other disciplines with a positive, collegial, collaborative attitude.
• Extensive knowledge of related disciplines (medicinal chemistry, HT screening, analytical chemistry, systems biology, DMPK, Tox, Imaging) to enable multidisciplinary approaches to be identified and integrated i