Junior Data Scientist Biomarkers Statistics - VIE Contract
Role details
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Tech stack
Job description
As Junior Data Scientist Biomarkers Statistics VIE within our Biomarker Statistics Team, you'll be a part of a young, dynamic, and international team of Data Scientists, Statisticians, and Bioinformaticians. In this role, you will contribute to bringing cutting-edge AI/ML approaches in-house to for find the right indications and boost the efficiency of drug development. You will analyse data with thousands of genes and proteins from clinical trials of Sanofi's key compounds for supporting clinical decision-making. You will collaborate closely with Biomarker Statistics colleagues worldwide (US, Europe, China, India).
Join the engine of Sanofi's mission - where deep immunoscience meets bold, AI-powered research. In R&D, you'll drive breakthroughs that could turn the impossible into possible for millions., * Conducting literature research and internalizing state-of-the-art AI algorithms; implement AI tools for efficient internalization of external data to increase efficiency in drug development.
- Supporting the analysis of high-dimensional biomarker data using a broad range of ML methods (Random Forest, Gradient Boosting, SVM, recent meta-learners, etc.) to identify predictive and pharmacodynamic biomarkers.
- Integrating multi-omics data using deep learning algorithms to better understand mechanisms of action of innovative drugs.
- Developing standard R and Python functions, as well as R Shiny applications, to share methods and results with study teams.
- Collaborating with diverse partners: programmers, biologists, clinicians, bioinformaticians, and more.
- Contributing to a cross-company working group on ML/AI applications in drug development, with emphasis on biomarker science.
- Sharing findings through scientific publications, seminars, and internal/external webinars.
Requirements
- At least 1 year of experience with R programming.
- Experience with transcriptomic data is highly preferred.
Soft and technical skills:
- Strong foundation in statistics and/or bioinformatics.
- Good understanding of deep learning and AI.
- Good knowledge of Python.
- Autonomous, rigor, team player.
Education:
- Master's Degree in Biostatistics or related fields (Mathematics, Computer Science, Statistics, ENSAI, ISUP, ENSAE, INSA, etc.).