Machine Learning Cheminformatics Engineer, Drug Discovery (EMEA)

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Requirements:

• PhD in chemistry, biology, computer science, or a related discipline.
• 1-5 years of relevant experience including hands-on experience with informatics, machine learning, and computational chemistry applied to drug discovery in the private sector, like biotech or pharma.
• Experience with cheminformatics and bioinformatics methods (e.g., similarity / substructure searching, reaction-based enumeration, sequence alignment, etc.).
• Experience with molecular property prediction and multi-objective optimization using machine learning and / or deep learning methods.
• Experienced with common python toolkits for scientific computing (e.g., numpy, pandas, scipy), machine learning (e.g., scikit-learn, pytorch), and cheminformatics / bioinformatics (e.g., rdkit, openeye, biotite, biopython).
• Familiarity running simulations and training models on high-performance computing (GPU) environments for corporate R&D, innovation labs, or academic research.
• An interest in solving scientific problems in chemistry and biology via computational and data-driven methods.
• A drive to cooperate with colleagues to identify problems and communicate technical solutions in an accessible manner.
• Hands-on mentality & comfortable with getting deep into the technical weeds of highly complex problems, and a track record of driving projects to completion.

Responsibilities:

• Design and implement software that leverages informatics, machine learning, and computational chemistry to address unmet needs in drug discovery.
• Contribute to ongoing research leveraging physics-based simulation, deep learning, and knowledge graphs for drug discovery applications.
• Work closely with an interdisciplinary team of scientists to identify hits and optimize leads in ongoing drug discovery programs.
• Leverage Bayesian optimization and active learning to improve experimental designs and make data-driven decisions.
• Collaborate with computational chemistry experts and cross-functional teams to rapidly prototype and scale cutting-edge, impactful drug design solutions.
• Translate research and applications to maintainable software systems.
• Contribute to the scientific community by writing patents / journal articles and presenting at conferences.
• Translate insights from statistics, multimodal data analysis, and ML to actionable and testable drug discovery hypotheses.