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 experienceTop 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.#J-18808-Ljbffr