Computational Scientist

Planet Pharma; South San Francisco (CA); Hybrid; USD 62,000 - 74,000; Full time

Job summary

Planet Pharma in the United States seeks a highly independent computational scientist in genetic epidemiology and statistical genetics to develop and apply analytical methods for integrating genetic, genomic, and clinical data. Hybrid or remote US-friendly time zone, 1-year contract with extension possible.

Responsibilities include analyzing large datasets, developing multimodal data integration approaches, and implementing ML to link imaging and omics data.

Qualifications

• PhD in Statistical Genetics, Computational Biology, Bioinformatics, or Genetic Epidemiology, or Masters with experience
• Fluent in R, Python, and shell scripting; some C++ familiarity
• Experience with Git and HPC environments (e.g., SLURM)
• Extensive experience in large-scale genetic/genomic data analysis
• Ability to work independently and meet deadlines with good communication

Responsibilities

• Collaborate with scientists in Human Genetics department to analyze large datasets of genetic, genomic, and clinical data from internal studies (including our clinical trials and high throughput screens), collaborations with academic and industry partners, and public external data sets
• Develop analytical approaches to integrate and interpret these data, delivering insights into disease biology to propel our translational goals
• Implement novel machine learning algorithms to understand associations between imaging and omics data
• Coordinate the intake and preparation of new datasets as they become available for analysis
• Document process, findings, and code
• Present findings to the department and cross-functional collaborators and contribute to publications

Skills

R

Python

Shell scripting

Data analysis

Education

PhD in Statistical Genetics / Computational Biology / Bioinformatics / Genetic Epidemiology

Masters with significant experience

Tools

Git

SLURM

C++

Job description

The Human Genetics department is seeking a highly independent computational scientist with a strong hands-on analytical background in genetic epidemiology, statistical genetics, or computational biology, to develop and apply analytical approaches to integrate and interpret genetic, genomic, and clinical data. We are particularly interested in candidates with skill sets that position them to tackle the integration of multiple sources of human biological data, such as whole genome sequencing and single cell RNA-Seq/ATAC-Seq data, including knowledge of emerging multimodal data integration methods. This will be a full time contract position for 1 year from date of hire, with the possibility of extension. This is available either as a hybrid or remote position and would ideally be based in a US-friendly time zone.

Responsibilities:

• Collaborate with scientists in Human Genetics department to analyze large datasets of genetic, genomic, and clinical data from internal studies (including our clinical trials and high throughput screens), collaborations with academic and industry partners, and public external data sets
• Develop analytical approaches to integrate and interpret these data, delivering insights into disease biology to propel our translational goals
• Implement novel machine learning algorithms to understand associations between imaging and omics data
• Coordinate the intake and preparation of new datasets as they become available for analysis
• Document process, findings, and code
• Present findings to the department and cross-functional collaborators and contribute to publications

Requirements:

• Extensive experience in large-scale genetic/genomic data analysis including one or more of the following areas of expertise:
• Understanding of principles of genetic epidemiology
• Association analysis with array- and sequence-based genetic data (GWAS – genome-wide association studies) on human data
• Analysis of sequence-based molecular assay data (eg RNA-Seq) including differential expression methods, single-cell sequencing data (eg scRNA-Seq, scATAC-Seq) and/or proteomic data
• Integration of genetic and molecular data for multimodal analyses
• PhD (or Masters with significant experience) in Statistical Genetics, Computational Biology, Bioinformatics, Genetic Epidemiology, or a related field
• Fluent in R, Python, and shell scripting. Some familiarity with C++ will be a plus
• Experience working with git and high performance computing (e.g. the SLURM scheduling manager)
• Curiosity and desire to learn more about human genetics, bioinformatics, and biology
• Ability to produce high-quality analysis results with minimal supervision. This includes meeting key deadlines and making sensible independent decisions
• Good communication skills and experience working as part of a team

Pay Rate Range: $45-54/hr depending on experience