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Posted: 05-Feb-26
Location: Los Angeles, California
Categories:
Internal Number: 150916814
We are seeking a highly motivated and skilled Genomic Data Manager to support and advance large-scale genomic research. The successful candidate will play a key role in managing, processing, and analyzing complex genomic datasets and developing scalable, reproducible workflows to facilitate discovery. Working closely with a diverse team of bioinformaticians, statisticians, programmers, and clinical researchers, the Genomic Data Manager will help ensure data quality, accessibility, and integrity across multiple research initiatives.
- Oversee data acquisition, curation, and organization across multiple research projects.
- Maintain data dictionaries, metadata, and documentation consistent with FAIR (Findable, Accessible, Interoperable, Reusable) data principles.
- Strengthen data governance practices to ensure compliance, security, and transparency.
- Collaborate with IT and data infrastructure teams to improve systems that support research data management.
- Work collaboratively with multidisciplinary teams to translate complex data into meaningful insights.
- Communicate findings effectively to both scientific and non-technical audiences.
- Mentor trainees and junior staff to support skill development and knowledge sharing.
- Contribute to the preparation of scientific manuscripts, reports, and presentations
- Bachelor’s degree in Computational Biology, Bioinformatics, Computer Science, or related field and at least two years of relevant experience, or an equivalent combination of education and experience.
- Five years of experience in R, Python, or Perl is required
- Three years of experience with Linux/Unix environments, HPC or cloud computing, and source control (Git)
- Four years of hands-on experience with NGS data analysis and workflow management tools (e.g., Nextflow, Snakemake).
- Design, develop, and maintain modular computational pipelines for genomic data validation.
- Optimize workflows for scalability and efficiency in high-performance computing (HPC) or cloud environments.
- Implement best practices in reproducibility through version control (Git) and containerization (e.g., Docker, Singularity).
- Participate in benchmarking and continuous improvement of bioinformatics tools and infrastructure
- Assist with bioinformatics and statistical analyses on large genomic and clinical datasets.
- Conduct quality control, data validation, and reproducibility checks.
- Assist investigators with genomic data queries and provide analytical and technical guidance.
- Apply rigorous analytical approaches to ensure robust and unbiased results
