SEC Recruitment

Lead AI Scientist & Omics - Bioinformatician

Location
France, Île-de-France, Paris / France, Rhône-Alpes, Lyon / Lille, France, Nord-Pas de Calais
Salary
Negotiable
Posted
10 Feb 2021
Closes
10 Mar 2021
Ref
SEC/Lead/Bioinfo/090
Contact
Yanick Millet
Hours
Full Time
Contract Type
Permanent
Experience Level
Experienced (non-manager)
Responsibilities:
* Lead and drive AI drug discovery projects, including genomics/omics data collection and analyses, target identification, network medicine, biomedical text mining, implementation/invention of new AI and omics analysis methods, and contribution to experiments design.
* Participate in regular meetings with the Chief Data Officer and other project leaders for overall department strategy.
* Collaborate on other projects within the data science department and the organization.
* Draft research manuscripts.
* Perform regular scientific literature reviews to share relevant discoveries and translate that into impact on the organization's research.


Requirements:
* A Ph.D. in bioinformatics or related field encompassing experience in machine learning, data science, and artificial intelligence applied to the biomedical area,
* At least two years of working experience following Ph.D. graduation with a track record of very high achievements,
* Strong experience in Python, especially machine learning packages (e.g., sci-kit-learn, TensorFlow, PyTorch) and Unix environments,
* Strong experience in drug discovery and/or network biology,
* Experience with big genomics/omics datasets, preferably in the context of GWAS and transcriptomics,
* Good knowledge of human genetics,
* Strong capacity to independently survey the latest research in the field and translate that into impact on your research,

Nice to have:
* Team lead experience in bioinformatics/data science,
* Knowledge in Natural Language Processing (NLP) and text mining,
* Experience with statistical/population/quantitative genetics and knowledge of Statistics,
* Experience with versioning, environment managing/containerization,
* Cloud computing and parallel computing (e.g., AWS),
* R language.

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