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Cheminformatics Data Scientist Intern · London

With over 35 nationalities and a range of backgrounds represented in our Benevolent team, we aim to build an inclusive environment where our people can bring their authentic selves to work, be respected for who they are and the exceptional work they do. We welcome and actively encourage applications from all sections of society and are committed to offering equal employment opportunities regardless of sex, race, religion or belief, ethnic or national origin, marital, domestic or civil partnership status, sexual orientation, gender identity, parental status, disability, age, citizenship, or any other basis. We see our diversity as an asset as we tackle challenging problems that bridge the gap between drug discovery and technology.


The Role

As a Cheminformatics Data Scientist Intern you will explore the latest updates regarding uncertainty in Quantitative Structure-Activity Relationship models and apply these to BenevolentAI models. You will ensure that the chosen uncertainty methods are appropriate and effective on a wide variety of model architectures and explore questions such as: How does uncertainty interact with applicability domains? And can a combination of uncertainty and applicability domains be used to identify accurate prospective predictions?

Primary Responsibilities

  • Build predictive models and apply innovative ideas and best practices in their generation and application to drug discovery projects.
  • Explore and apply the latest methodology in quantifying model uncertainty
  • Collaborate and communicate effectively with members of the Chemoinformatics, Bioinformatics, Drug Discovery, Artificial Intelligence, Data Science, Engineering, UX and Product teams.

We are looking for someone with

  • Someone with a PhD (or currently working towards completion of a PhD) in the field of Chemoinformatics, Computational Chemistry, Molecular Modelling, Machine Learning applied to Chemistry, or a closely related field.
  • Competent in Python.
  • Good understanding of machine learning and artificial intelligence.
  • Excellent communicator, especially when working with colleagues from other specialities.
  • Familiarity with chemical descriptors and property predictors.
  • Bonus points for experience in drug development.
  • Familiarity with popular machine learning libraries would be advantageous e.g. scikit-learn, PyTorch, TensorFlow.


Applications close on February 12th, 2021. In addition to your CV, please send us a cover letter.

Terms and Conditions

Salary: We offer remuneration for the duration of the internships. However, remuneration remains subject to the University regulations & T&C’s - should they not comply with our policy, we reserve the right to review the salary allowance.

We share a passion for being part of a mission that matters, and we are always looking for curious and collaborative people who share our values and want to be part of our journey.  If that sounds like a fit for you, hit the apply button and join us.

About us

BenevolentAI (AMS: BAI) is a leading, clinical-stage AI-enabled drug discovery and development company listed on the Euronext Amsterdam stock exchange. Through the combined capabilities of its AI platform, scientific expertise, and wet-lab facilities, BenevolentAI is well-positioned to deliver novel drug candidates with a higher probability of clinical success than those developed using traditional methods. The Benevolent Platform™ powers a growing in-house pipeline of 13 named drug programmes and over 10 exploratory programmes, and it maintains successful collaborations with AstraZeneca, as well as leading research and charitable institutions. BenevolentAI is headquartered in London, with a research facility in Cambridge (UK) and a further office in New York.

Want to do a little more research before you apply?

Head over to our Glassdoor page to learn about our benefits, culture and to find out what our team thinks about life at Benevolent. You can also find out more about us on LinkedIn and Twitter.

Important Note

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