Computational Scientist - (Machine Learning) Digital R&D Large Molecule Research

Sanofi

Cambridge, United Kingdom

You are a dynamic Computational Scientist who will work with other scientists to apply cutting-edge computation, Machine Learning/Deep Learning approaches to revolutionize our large molecule computational tools by contributing to accelerating and improving the process of design and engineering of novel biologics drug candidates.

Main responsibilities:

Apply and develop artificial intelligence and machine learning (AI/ML) approaches (e.g. classification, clustering, machine learning, deep learning) on pharma research data sets (e.g. activity, function, ADME properties, physico-chemical properties, etc.).
Building models from internal and external data sources, algorithms, simulations, and performance evaluation by writing code and using state-of-the art machine learning technologies.
Close interactions with other Computational scientists, data engineers, software engineers, UX designers, as well as research scientists in core scientific platforms focusing on protein therapeutics, in an international context (US, Europe).
Update and report relevant results to interdisciplinary project teams and stakeholders.

About you:

PhD in a field related to AI/ML or Data Analytics such as: Computer Science, Mathematics, Statistics, Physics, Biophysics, Computational Biology or Engineering Sciences.
Ideally 1+ years of industry experience, new grads will also be considered. Should have a track record of applying ML/Deep Learning (DL) approaches to solve molecule-related problems.
Familiarity with protein structure or sequence featurization/embeddings.
Familiarity with advanced statistics, ML/DL techniques including various network architectures (CNNs, GANs, RNNs, Auto-Encoders, Transformers, PLM etc.), regularization, embeddings, loss-functions, optimization strategies, or reinforcement learning techniques.
Proficiency in Python and deep learning libraries such as PyTorch, TensorFlow, Keras, Scikit-learn, Numpy, Matpilotlib.
Familiarity with data visualization and dimensionality reduction algorithms.
Ability to develop, benchmark and apply predictive algorithms to generate hypotheses.
Comfortable working in cloud and high-performance computational environments (e.g. AWS).
Excellent written and verbal communication, strong tropism for teamwork.
Strong understanding of pharma R&D process is a plus.

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