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Senior Data Scientist – Drug Discovery

ABG-87494 Emploi Confirmé
21/09/2019 CDI Salaire à négocier
BioXcel Therapeutics, Inc.
Prague - Tchèque, République
Informatique
  • Biologie
Bioinformatics, data science, Python, NumPy, SciPy, Pandas, Sklearn, Keras, R, Bioconductor, Perl, AI, exploratory statistics
Recherche et Développement

Employeur

BioXcel Therapeutics is a growing US-Indo clinical-stage pharmaceutical company committed to using artificial intelligence and data science to address critical challenges in drug discovery.  We believe the tight integration of AI with medicine and science can empower the rapid development of innovative therapies to address important unmet clinical needs, and we pride ourselves on our spirit of innovation, collaboration, smart risk-taking, and state-of-the-art expertise.  Our team is made of professionals from different disciplines and career paths but share a common goal: to accelerate the development of novel therapies for people who need them using AI/data science.

If this type of environment sounds exciting, we would encourage you to look at the details!

Poste et missions

  • Build predictive models using data from molecular and clinical sources, as well as the public literature.
  • Prepare and deliver presentations to team members and senior leadership with clear, objective communication of insights and opportunities.
  • Design, build, and manage reusable software pipelines and manage internal systems for high efficiency.
  • Maintain cutting-edge knowledge of advances in AI/ML, digital health, and drug repositioning.
  • Prioritize multiple projects, help manage infrastructure.

Mobilité géographique :

Internationale

Profil

Qualification and Experience: Masters Degree in Bioinformatics, Molecular Biology, Computer Science, Engineering, or related field, with 5-10 years of outstanding experience, or Ph.D. 3-5 years of outstanding experience. Patents and published articles in international journals are desirable.

Recommendations are required.

Required Skills:

  • Computing Languages:  Python (NumPy, SciPy, Pandas, Sklearn, Keras), R (Bioconductor), Perl.
  • AI/Data Science:  exploratory statistics, all supervised and unsupervised classification/prediction techniques, feature selection, cross-validation, performance evaluation, visualization.
  • Software Development:  GitHub, JIRA, Linux.

Desired Skills

  • Knowledge of network models and graph-based models and associated computing technologies.
  • Knowledge of cloud-based resources and technologies, like AWS, Azure, etc.
  • General understanding of the drug R&D process.
  • Background in wearable technologies.

Objectifs

Job Role and Summary:  We are seeking to add a dynamic data scientist. This is an important role since this person’s ability to apply data science/AI effectively directly impacts many aspects of the company’s future. The candidate will use machine learning techniques to construct and analyse predictive models that use both molecular and clinical data as well as data from the public literature, to identify and validate drug candidates for repurposing/repositioning.  The candidate should be able independently justify what data best addresses a problem, where to get it, and how to use it, ideally capitalizing first on all public data sources and repositories. Creatively using AI/ML to discover subpopulations from complex data sets where drugs have high efficacy and safety is very important, as well as the ability to visually render and communicate results to multi-disciplinary audiences.  

A candidate should have demonstrated experience introducing state-of-the-art data science into challenging biomedical problems and extracting useable and novel insight. This person will intuitively recognize that the right answer may require multiple iterations and demand incorporating multiple perspectives, and he/she will anticipate this in their thinking and planning. A candidate should also have experience using state-of-the-art software development and collaboration technologies to build reusable software pipelines that could involve more work initially but pay off substantially in the long run.

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