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Bioinformatician

Leonard Davis School of Gerontology Los Angeles, California

The University of Southern California (USC), founded in 1880, is located in the heart of downtown Los Angeles and is the largest private employer in the City of Los Angeles. USC is consistently ranked among the nation’s most prestigious universities, and the USC Leonard Davis School of Gerontology features one of the world’s best degree and research programs in gerontology. The USC Leonard Davis School has an international reputation as a hub of aging research and with additional strong programs throughout the university leads the way in defining and advancing the field of gerontology.
An exciting opportunity has been created for an enterprising Bioinformatician to join the USC-Buck Nathan Shock Center (NSC) Genomic Translation Across Species Core (GTASC) within the Leonard Davis School of Gerontology’s Bioinformatics Core at the University of Southern California (USC). The mission of the Gerontology Bioinformatics Core (GBC) is to facilitate the development of data analytic procedures to enable the translation of big data into research findings. The Bioinformatics Core has growth opportunities with funded research projects that integrate multiple disciplines, including aging biology, geroscience, lifespan, and developmental psychology, genetics, demography, and public health. In particular, we have a strong focus on using genomic data derived from diverse human cohorts to validate findings in model organisms to better understand the architecture of complex traits related to aging-related diseases and longevity. For more information about the GBC (https://gero.usc.edu/bioinformatics-core/) or the Davis School of Gerontology (https://gero.usc.edu), click on the included links

Responsible for creating and running analytical pipelines to answer research questions on funded projects. Serves as an integral part of the research team. Works closely with faculty and research leads to develop and implement coding approaches. Educates and cross-trains graduate students, staff, or post-docs in external collaborating groups. Makes independent decisions on analysis approaches, free from immediate direction, within scope of responsibilities.

The GTASC focuses on testing hypotheses in humans that were generated from research conducted in model organisms to aid in identifying processes that influence health and lifespan. We do this using genomic data (genetic, epigenetic, gene expression) collected as part of large population-based studies. The bioinformatician would be responsible for creating and running analytical pipelines to answer research questions that are instrumental in accelerating the pace of translational genomics relevant to human aging.  The bioinformatician will be an integral part of the research team and work closely with the core leaders to develop, implement, and document these pipelines. Demonstrated knowledge of programming and/or scripting languages (i.e., R, SAS, python) is required for this position. The ideal candidate will be a person who has demonstrated skills in genomics data analysis (e.g., GWAS, EWAS, TWAS, etc.), using high-performance computing clusters, managing large data files, documenting methods used for bioinformatic analyses, applying statistical methods for work with high dimensional data. High attention to detail, good organization, and communication skills are essential.

The successful candidate will perform quality control, data management, and analysis of data using biosocial, behavioral, health, environmental, and genetic/epigenomic measures; assist with annotation of findings; prepare data for reports and manuscripts, and grant applications; stay abreast of new technology and analytic techniques; and present research activities within the group and with collaborators. The bioinformatician will have the authority to make independent decisions on matters of significance, free from immediate direction, within the scope of their responsibilities. Flexible working environment. Posting will remain open until a suitable candidate is identified.  

Key Accountabilities

  • Evaluate and assess epigenome- and genome-wide data in large population studies of older adults (e.g. Health and Retirement Study, English Longitudinal Study of Ageing). Independently analyze and interpret data analyses from quality control to regression models. This includes developing/implementing quality control protocols, generating epigenetic or genetic measures (epigenetic age, polygenic scores, etc), and conducting analyses (e.g. EWAS, GWAS, SKAT, etc). Annotate findings.
  • Consult with collaborators to clarify, improve and evaluate research projects. Manage data acquisition, storage, and sharing tasks, such as data use agreements and interfacing with data repositories (e.g. dbGaP, NIAGADS, and GEO). 
  • Perform regular maintenance of datasets including merging refreshment files, restructuring and recoding variables, and checking for errors (while providing feedback to analysts during this process).  Create and maintain clear and organized workflow documentation for internal and external review. Chronicle data management and analysis steps and annotate program files to ensure full transparency and reproducibility as part of a public analysis pipeline (interface with Github, etc.) to accompany manuscripts.
  • Perform code review for other analysts as part of the team, including working with other team members to fix errors and discuss differences in scientific opinion. Assist in the supervision of students/staff/postdocs, including guiding and managing tasks. Conduct literature reviews on aging, genomic, and bioinformatic topics. Write data, analysis, and results sections for working papers, reports, grants, and manuscripts. Collaborate in the development of data presentations for conferences and workshops.

The GBC fosters a work environment that is inclusive as well as diverse, where ideas and perspectives are valued. We are building a team where individuals can thrive, contribute to cutting-edge research related to health with aging, and collaboratively elevate translational science. We welcome applicants from all backgrounds and do not discriminate based on race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, disability, veteran status, marital status, or an individual’s status in any group or class protected by applicable federal, state, or local law.

Required Qualifications

  • Master’s degree in a recognized field of science or learning which is directly related to the duties of the position, including, but not limited to, biostatistics, quantitative social science, molecular genetics, statistical genetics, translational genomics, or bioinformatics.
  • Two or more years of experience in at least two domains characteristic of the bioinformatics, including at least one biological/biomedical science and one quantitative/informatics/computing science, with an aptitude for others.
  • Experience analyzing large and complex biological and/or -omics datasets using reproducible analysis approaches (e.g. analysis pipelines).
  • Data management and statistical modeling programming experience in R or other –omic analysis software using command line programming such as Linux/Unix.
  • Highly motivated to learn and implement novel quality control and analyses techniques for genomic data.
  • Ability to multi-task in order to facilitate several research projects at different stages of completion.
  • Experience with record-keeping for documentation of analyses and analysis pipelines.
  • Good communication on technical and non-technical aspects of data analysis, tasks, and timelines.
  • Must be able to work independently and as part of a research team.
  • Familiarity with statistical genetic and social science research methods.

Preferred Qualifications

  • Familiarity with statistical genetic and social science research methods.
  • Experience with genomic analysis of large health or social science studies.
  • Programming experience with R Markdown, Python, and R shiny apps.
  • Experience with standard bioinformatics software tools and packages e.g. Bioconductor.
  • Experience analyzing large and complex biological and/or -omics datasets using reproducible analysis approaches (e.g. analysis pipelines).

Work Locations

This position will be on-site at the University of Southern California, Leonard Davis School of Gerontology, with flexible scheduling and remote opportunities made available within our overall school policies. If relocation is required, we will allow for reasonable time and flexibility to make necessary arrangements. 

Additional Information

The University of Southern California offers a competitive benefits package including medical, dental, vision, life insurance, accidental death, and dismemberment insurance, and short and long term disability insurance.

The annual base salary range for this position is $80,000-$96,000. When extending an offer of employment, the University of Southern California considers factors such as (but not limited to) the scope and responsibilities of the position, the candidate’s work experience, education/training, key skills, internal peer equity, federal, state and local laws, contractual stipulations, grant funding, as well as external market and organizational considerations.

REQUIRED DOCUMENTS: In order for your application to be considered, a cover letter and resume must be attached.


Minimum Education: Master's degree Minimum Experience: 2 years Minimum Skills: Experience analyzing large and complex biological/-omics datasets using reproducible analysis approaches (e.g., analysis pipelines). Demonstrated knowledge of programming/scripting languages (e.g., R, SAS, python). Programming experience in data management and statistical modeling (e.g., R or other –omic analysis software, using command line programming). Demonstrated skills in genomics data analysis (e.g., GWAS, EWAS, TWAS, etc.); experience using high performance computing clusters, managing large data files, documenting methods used for analytical procedures, and applying statistical methods for working with repeated measures data (e.g., from large-scale, population-based datasets). Highly motivated to learn and implement novel quality control and analyses techniques. Ability to multi-task. Experience with analysis pipeline record-keeping. Strong organizational and communication skills. High level of attention-to-detail. Ability to work independently and as part of a research team. Familiarity with statistical genetic and social science research methods. Preferred Skills: Experience with genomic analysis of large health or social science studies. Programming experience with R Markdown, Python, and R shiny apps. Experience with standard bioinformatics software tools and packages (e.g., Bioconductor). Experience analyzing large and complex biological/-omics datasets using reproducible analysis approaches (e.g., analysis pipelines).


REQ20122350 Posted Date: 12/18/2022

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