To advance the development of small molecule therapies for people with serious and life-threatening diseases, we are seeking a computational biologist with expertise in making biological discoveries by leveraging large-scale genomic data. You will be a member of the Computational Genomics team that designs and analyzes genomic studies across the drug discovery pipeline. Your work will impact many areas, such as target discovery and validation, biomarker characterization, and the identification of treatable patient populations. Your key tasks will be to collaborate with cross-functional project teams to design studies, analyze and interpret large-scale genomic data, and communicate results that will have critical impact on the development of transformative therapies. You will play a key role in bringing together the needs of project teams with state-of-the-art computational biology
tools, which will require proactive communication and seamless collaboration with both computational and experimental team members.
Key responsibilities:
• Work closely with disease project teams, acting as the computational biology expert, to identify key biological questions that can be addressed using genomics, sequencing, or bioinformatics.
• Plan studies leveraging sequencing, genetic, and genomic data to answer critical biological questions and drive the development of transformative therapies.
• Plan, execute, and iterate on the analysis of large-scale sequencing data, in partnership with computational and experimental team members.
• Visualize, summarize, and communicate findings to project teams and other key stakeholders.
• Partner with team members to improve or expand computational methods and develop best practice analytical workflows
Minimum Qualifications:
• PhD degree in computational biology, bioinformatics, human genetics, genomics, systems biology, or a related field. Expertise in one or more of the following areas is highly desired: gene expression (bulk/single-cell RNA-seq),
DNA sequencing (whole-genome/exome, targeted), pooled genetic screening (genome-wide CRISPR screens, saturation mutagenesis), statistical genetics (GWAS, QTLs), epigenetics/epigenomics, and bioinformatics method development.
• A proven track record in the analysis, visualization, and interpretation of genomic and next-generation sequencing (NGS) data to generate novel biological insight. Demonstrated ability to work closely with wet lab collaborators to design studies and develop analyses is a plus.
• Solid understanding of statistics and genetics, including the role of genetic variation in human disease.
• Expertise in applying computational methods and bioinformatics tools to large-scale data; proficiency with R or Python, Linux/Unix systems, and high-performance computing systems. Experience with workflow language and cloud computing is a plus.
• Excellent presentation and communication skills, including the ability to tailor scientific content to audiences with different backgrounds.