As the Head of In Silico Target Discovery you will lead high-impact research projects at the interface of machine learning, computational biology, bioinformatics and drug discovery & development.

These projects will be focused on building machine learning models from multimodal data (genomic data, histology, radiology, clinical etc), to identify new biomarkers, therapeutic targets, drug combinations, indications expansions and optimized drug development strategies.

You will be part of the R&D team and will collaborate with Our Client's scientists and/or external collaborators (academic researchers / pharmaceutical companies) to deliver these research projects centered around specific cancer diseases.

In particular, you will:

Lead research projects interacting with academic partners and/or pharma (involving publications, mentoring of junior scientists etc) to better understand disease mechanisms, and to generate new hypotheses about potential therapeutic strategies

Develop internal research projects to advance core scientific methodologies, focusing on the development of methods leveraging multi-modal patient data (multi-omics as well as histology, radiology and clinical data) to improve the precision of biomarker and target identification approaches

Work in collaboration with other of Our Client's departments to support marketing, business development & communications activities.

*The responsibilities missions described are not an exhaustive list; additional tasks may be assigned or the scope of the job may change as necessitated by business demands.*

Position is based in our Paris or London offices or remotely.

About you

Required qualifications / experience:

PhD or MSc in Computational Biology, Bioinformatics, Machine Learning, Computer Science, Statistics or related field with relevant industry or academic experience

Prior experience in leading large scientific projects with demonstrated ability to write detailed research plans and reports

Demonstrated ability to lead several scientists to achieve scientific objectives

Strong programming skills in Python and/or R with demonstrated knowledge of exploratory analysis and data visualisation.

Expertise working with -omics data, in particular in single cell and spatial omics technologies, with a strong publication record.

Experience working in target or drug discovery settings is a plus.

Familiar with machine learning toolkits and concepts (scikit learn, xgboost, cross validation, statistical tests etc. Experience with deep learning frameworks (Tensorflow, PyTorch, etc.) strongly preferred.

Team first, highly collaborative mindset.

Excellent written and oral communication skills. Fluent in English. Fluent in French is a plus.

Highly organised and meticulous about details; capable of tackling complex problems logically and effectively; fun to work with; honest; excited to work hard in a dynamic, fast-paced environment; ...

Passionate about the intersections of healthcare and AI

Proficiency in next-generation sequencing data analysis (RNA-Sequencing, DNA-Sequencing, etc...)

Previous experience with target identification approaches in oncology is a plus

What we offer

Competitive salary & excellent benefits package

Flexible work organization and access to remote work

Friendly and informal working environment

Opportunity to work with an international team with high technical and scientific backgrounds

Contact Gail Kniveton for more information 

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