Bioinformatics Scientist - Cancer Genomics & AI

The Lundquist Institute; Torrance (CA); On-site; USD 120,000 - 180,000; Full time

Job summary

The Lundquist Institute seeks a Bioinformatics Scientist to lead computational analyses for cancer and translational research. You will develop and apply state-of-the-art approaches to NGS and multi-omics data, integrating diverse genomic and related data.

Collaborate with investigators, statisticians, and external partners to generate meaningful insights and high-impact publications. The role requires staying current with emerging computational methods and AI applications in biomedical

Qualifications

• PhD in Bioinformatics, Computational Biology, Genomics, Biostatistics, Biomedical Informatics, Computer Science, or a closely related quantitative discipline.
• More than three years of relevant postdoctoral or post-PhD experience in bioinformatics, computational biology, genomics, cancer research, or related fields.
• Demonstrated expertise in NGS and multi-omics data analysis.
• Strong record of scientific contributions, including peer-reviewed publications involving cancer genomics or omics research.
• Experience analyzing cancer-related genomic, transcriptomic, epigenomic, and other omics data.
• Experience accessing, curating, and analyzing public annotated genomic and NGS databases.
• Understanding of spatial sequencing technologies, spatial transcriptomics, protocols, and analyses.
• Proficiency in R and familiarity with additional bioinformatics software tools, statistical packages, and pipelines.
• Experience developing and implementing analytical pipelines and computational methods.
• Strong knowledge of biostatistical methodologies and their application to biological datasets.
• Ability to evaluate, implement, and optimize emerging computational technologies.
• Ability to communicate complex scientific findings through publications and presentations.
• Excellent analytical, organizational, problem-solving, and scientific communication skills.
• Demonstrated ability to work effectively in multidisciplinary collaborative research environments.

Responsibilities

• Lead the analysis and interpretation of NGS and multi-omics datasets, including genomic, transcriptomic, epigenomic data.
• Design, develop, validate, and implement computational workflows and bioinformatics pipelines to support cancer and translational studies.
• Perform advanced analytical assessments of omics datasets and identify biomarkers, signatures, and disease-related mechanisms.
• Access, curate, and analyze publicly available annotated genomic, transcriptomic, and cancer databases.
• Analyze spatial sequencing and spatial transcriptomics datasets and understand related protocols.
• Apply AI/ML methodologies to enhance bioinformatics workflows where appropriate.
• Use biostatistical methods with R and other tools.
• Collaborate with investigators, clinicians, statisticians, and external partners to define objectives.
• Generate publication-quality figures, tables, datasets, and methods descriptions.
• Contribute to manuscripts, presentations, abstracts, and grant applications.
• Maintain organized reproducible workflows, documentation, and data-management practices.
• Stay current with advances in bioinformatics and omics analytics.
• Perform other duties to support research objectives.

Skills

NGS data analysis

Multi-omics data analysis

R programming

Biostatistics

Bioinformatics pipelines

Machine learning

Scientific communication

Cloud computing

Education

PhD in Bioinformatics or related field

Tools

R

Bioinformatics software

HPC environments

Cloud computing

Job description

The Lundquist Institute seeks a Bioinformatics Scientist to lead computational analyses for cancer and translational research. You will develop and apply state-of-the-art approaches to NGS and multi-omics data, integrating diverse genomic and related data.

Collaborate with investigators, statisticians, and external partners to generate meaningful insights and high-impact publications. The role requires staying current with emerging computational methods and AI applications in biomedical