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Molecular Data Scientist - Biologics (Fixed term Contract) at Novo Nordisk
London, United Kingdom


Job Descrption

   

The Position

We are searching for a talented Molecular Modelling and/or Data Scientist with strong interest in protein design and machine learning techniques.

 

Your main role will involve enhancing our internal Modelling/AI workflow for data-driven compound design, including the integration of novel ML technologies for protein structure predictions, biophysical property predictions, and variant design. Another aspect of the position will be to drive the development of novel SAR visualization and analysis tools and ensure its best usage within selected projects. The position requires close collaboration with other research scientists in multidisciplinary project teams as well as our data science and machine learning colleagues across the global sites of the Digital Science & Innovation area.

 

Your key responsibilities will entail to:

  • Develop predictive models for biologics design as well as SAR analysis and visualization methods and integrate them into the in house in silico pipeline to enable multi-objective compound design and data-driven decision making.
  • Apply predictive models for biologics design, including developability engineering and experimental design of high-throughput variant libraries.
  • Review technology landscape and build external collaborations with key partner in the AI field for biologics.

The ideal candidate has a general curiosity and excellent track record within drug discovery, molecular modelling, and/or machine learning technologies, can identify and address key project challenges, can collaborate well within a multidisciplinary environment, has a positive attitude towards challenges, takes the initiative for driving projects, and can stay on top of things in a dynamic working environment.

This position will be in our new London Office, at King’s Cross.

This is a 12-month fixed-term contract, operating on a hybrid model with 2 days working from the office.

 

Qualifications

To be successful in this role, we expect you to have the following qualifications:

 

  • PhD degree within a computational chemistry or biology discipline, such as Computational Chemistry, Molecular Modelling or Bio-/Cheminformatics
  • Strong relevant experience of drug discovery experience in relevant pharma/biotech R&D environment or comparable track record of post graduate experience in academia.
  • Proven track record and in-depth knowledge in the development and application of AI/ML for de novo design and/or compound property predictions, preferably for biologics
  • Broad knowledge of cheminformatics, bioinformatics, and QSAR methods and experience with the development of related data analysis pipelines and visualization
  • Experience using protein modelling software such as Rosetta, Schrodinger and/or MOE.
  • Fluent in Python, data-workflow tools, and data science packages like scikit-learn and pandas

 

We're seeking a highly collaborative individual who is eager to learn and can work independently. You should be able to effectively engage with various stakeholders and excel in cross-functional teamwork.

 

About the Department

The Protein Engineering department is part of Novo Nordisk’s Computational Drug Design (CDD) area where we apply and develop state-of-the-art capabilities in structure-based modelling and AI/ML approaches for drug design. Scientists within CDD collaborate seamlessly with teams across the globe to invent better molecules faster. With an unbiased approach to drug modalities, in-house access to the newest screening techniques, and world-class experimentation, we have a wealth of opportunities to impact our pipeline and ultimately save and improve patient lives. We work in multidisciplinary, highly collaborative discovery teams both locally and across Novo Nordisk’s global network to invent novel medicines by applying advanced computational techniques and developing innovative computational methods. We also engage in external collaborations to ensure access to cutting edge research and technology.

The Protein Engineering team is focusing on the design of biopharmaceutical proteins such as protein minibinder and protein replacement therapies, using Molecular Modelling and Machine Learning approaches. We collaborate closely with our experimental colleagues in early research to improve and accelerate the design of new drug candidates. With a team of scientists located in Denmark, United States and the United Kingdom, we strive to deliver the latest advancements in protein therapeutics to benefit patient health and global medicine.

 

Working at Novo Nordisk

We are a proud life-science company, and life is our reason to exist. We’re inspired by life in all its forms and shapes, ups and downs, opportunities, and challenges. For employees at Novo Nordisk, life means many things – from the building blocks of life that form the basis of ground-breaking scientific research, to our rich personal lives that motivate and energise us to perform our best at work. Ultimately, life is why we’re all here - to ensure that people can lead a life independent of chronic disease.

 

Contact

If you are interested in the opportunity, please submit your application through our career site, as unfortunately, we do not accept directly sent resumes.

 

Deadline

Please apply before end of 12th May 2024.

 

We encourage you to apply early as we start engaging with suitable profiles on an ongoing basis.

 

We commit to an inclusive recruitment process and equality of opportunity for all our job applicants. 

 

At Novo Nordisk we recognize that it is no longer good enough to aspire to be the best company in the world. We need to aspire to be the best company for the world and we know that this is only possible with talented employees with diverse perspectives, backgrounds and cultures. We are therefore committed to creating an inclusive culture that celebrates the diversity of our employees, the patients we serve and communities we operate in. Together, we’re life changing.

 


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