Data Scientist - San Francisco, United States - Mitchell Martin

Description

Title: Computational Biology Data Scientist

Position Type: 1 year + extensions.

Location:San Francisco, CA / Hybrid

Job Description:

• Seeking a highly qualified and motivated Data Scientist with a strong background in Computational Biology.
• The data scientist will join the Bioinformatics Technology team within Client's Center for Research Acceleration by Digital Innovation (CRADI).

Responsibilities will include, but are not limited to:

• Develops computational integrative omics models for the inference of multi-dimensional immunological signatures that can serve as pharmacodynamic and predictive biomarkers.
• Identifies opportunities and delivers appropriate quantitative methods, such as AI/ML and causal learning, to push the state of the art for omics-based research biomarker discovery.
• Enables and executes in silico perturbation analyses to predict cellular response to drug or genetic perturbations.
• Develops innovative cloud-native bioinformatics workflows for rapid and sustainable omics data processing, quality control, and multi-omics data integration for systemic insights generation of disease biology and therapeutic mode of action.
• Works cross-functionally with stakeholders and domain experts to build a comprehensive catalog of gene signatures for all Client-relevant therapeutic areas.
• Interprets and reports on multi-dimensional studies using bulk, single-cell, and spatial omics technologies that will accelerate the pace of research biomarker discovery and translation in early drug discovery across all Tas.
• Collaborates with engineers, data scientists, and research scientists to develop innovative bioinformatics tools that facilitate data access, model utilization, and output interpretation.
• Experience: PhD in computer science, data science, computational biology or bioinformatics, preferably including industry experience OR Masters degree & 5+ years of directly related experience OR Bachelors degree & 7+ years of directly related experience

Top must have:

• Deep experience in and theoretical understanding of statistical modeling, machine learning, deep learning, causal learning, and other relevant quantitative methods.
• Proven knowledge in molecular biology (knowledge of inflammation biology is a major plus).
• Experience in integrative analysis of omics data for modeling of biological systems, including analysis of single-cell and spatial sequencing data.
• Ideally experience with in silico modeling of cellular response to drug or genetic perturbation.