Senior Data Scientist - Computational Biology

October 29

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Valo Health

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💰 Series C on 2022-10

Description

• About Us: Valo Health aims to accelerate the drug discovery and development process. • About the Role: Join the computational biology core in the Translational Data Sciences group. • What you’ll do: Integrate omics data, ensure data quality, perform analyses, and provide expertise. • What you bring: Educational background and experience in bioinformatics and computational biology.

Requirements

• BS+4, MS or PhD + 1 years experience in bioinformatics, computational sciences, computational biology, or related fields (e.g., genetics, molecular biology) in collaborative settings to unravel complex biological data challenges and communicate domain knowledge to non-computational stakeholders & colleagues • Experience in modeling multi-omic (2 or more: genomic, transcriptomic, proteomic, and/or metabolomic) data with statistical/machine learning methods & biological network analyses towards understanding biological functions and disease processes • Experience with high-dimensional omics data and their challenges, including biological, experimental, and computational sources of noise & variance, and approaches to address multi-collinearity • Strong analytical, problem-solving, and communication skills, including facility with Rmarkdown and/or Jupyter Notebooks for communicating reproducible results • Ability to condense, summarize, and synthesize results into informative and actionable presentations to scientific audiences as demonstrated by original peer-reviewed publications in respected journals, oral presentations at scientific meetings. • Experience in R and/or Python, including familiarity with code, data, and model versioning • Experience with OMICs data processing workflows (e.g., nextflow, snakemake), evaluating QC metrics and adjusting for batch effects, and working in cloud environments (e.g., AWS) • Undergraduate or graduate level course work in at least two of the following: Cell Biology, Microbiology, Developmental Biology, Physiology, Immunology, Genetics, Epidemiology, Evolution, Biochemistry, Organic Chemistry, History of Science • Domain knowledge in cardiovascular disease and its co-morbidities (e.g., obesity, diabetes, and inflammation) preferred • Experience with network biology (eg, WGNCA) approaches to a plus • Familiarity with public data sources (eg, GEO, CMAP/LINCS, etc) a plus