Molecular Data Scientist - Biologics
Lifelancer
- London
- Contract
- Full-time
- 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 Kings Cross.
- This is a 12-month fixed-term contract, operating on a hybrid model with 2 days working from the office.
- 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 RD 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.