The Bioinformatics Group in the Department of Biomedical and Health Informatics (DBHI) at The Children’s Hospital of Philadelphia (CHOP) is seeking a level II Bioinformatics Scientist to join our over 20 professional bioinformatics scientists in the Bioinformatics Group within the Department of Biomedical and Health Informatics. The Bioinformatics Scientist will be supporting analytics for NIH-funded, nationally visible projects in partnership with the Division of Oncology and Center for Childhood Cancer Research (CCCR) and the Center for Data Driven Discovery (D3B) at CHOP. This position will be embedded within a highly collaborative environment, and will initially be focused on task-driven data processing, data harmonization, and data analytics for a national data integration project for pediatric cancer.
The Bioinformatics Scientist will attend project meetings and interact daily with bioinformatics team members and individual lab members on a project-by-project basis. The Scientist will be supervised by an experienced bioinformatician and work within a multi-disciplinary team within CHOP and with external collaborators.
The successful candidate will have had either academic or on-the-job training on subjects related to cancer biology. They must have demonstrable productivity in bioinformatics, and at least five years of experience (inclusive of focused academic training) in bioinformatics projects utilizing Python programming and/or statistical analysis of bioinformatics data, such as with R and Bioconductor.
The successful candidate must have experience in at least two of following areas:
- Experience setting up pipelines and working in high-performance and/or cloud-based computing environments towards bioinformatics data processing for large-scale projects.
- Experience in the analysis of sequencing data related to cancer (e.g. somatic single nucleotide variants (SNVs) in indels, structural variations (SVs), fusion transcripts, or copy number analyses).
- Hands-on experience in general bioinformatics analyses, particularly those involving analysis of high-throughput data using Python, or R and Bioconductor.
- Demonstrable experience in project-level data harmonization and integration, including phenotype and genotype harmonization for multi-omics datasets, for cancer data resources is a plus.
- Experience extracting and working with data from electronic medical records (EMRs) is a plus.
The successful candidate’s experience with established methods for processing genomics data should allow them to develop and benchmark tests of performance of analytical methods as benefits the project.
The successful candidate should be able to work in cross-site teams on deadlines and have strong communication and listening skills. The candidate must be able to manage multiple projects and be prepared to work both independently and on collaborative efforts to complete projects within expected timelines.
The candidate should be ready to commit to full data and research reproducibility.
The candidate will be asked to give a project presentation and complete a basic programming and analytical task as part of the interview process.
Pre-Analysis (20%): Contribute to the development of application portfolio by developing knowledge of internally developed systems, open-source programs, and commercial applications. Provide efficient data management support.
- Use standard pipelines for data processing and manipulation in advance of performing analysis in a manner that best enables the analysis plan.
- Contribute to the development of additional pipeline functionality and changes by providing knowledge of both collaboration-specific requirements and bioinformatics discipline advances.
- Advocate for specific collaboration requirements for continual advancement of shared pipeline and code resources.
- Provide collaboration-specific transparency for data processing and pre-analysis, including sample- and cohort-level status.
Coding (20%): Code and generally support code and applications on behalf of collaborative project and/or team.
- Within the context of the collaboration or project, develop and apply best practices to code development.
- Establish requirements with the project team.
- Review existing applications and code sources (both commercial and open source) and selection of best strategy for development or adoption.
- Advocate for chosen strategy to project team by showing value of approach
- Develop best practices for project-based code development, QC, and execution consist with the expectations of specific collaborations.
- Regularly seek peer-to-peer code reviews by participating in informal and formal critical code reviews.
Data Analysis (20%): Analyze data of high complexity by applying sound statistical and commonly accepted bioinformatics methods to -omics data primarily under the direction of the collaborative project team.
- Develop robust analysis plans independently with regular peer-to-peer review in both informal and formal settings.
- Incorporate more advanced applications and methods into analysis.
- Develop at least one “specialty” analytical or biomedical area that serves the collaborative team.
Collaboration (20%): Establish role within collaborative project team as primary bioinformatics resource.
- Contribute to and influence project-level management by serving as bioinformatics point.
- Define and promote boundaries of support by assessing all stakeholders, including bioinformatics management, collaborator expectations, and funding levels and mechanisms.
- Regularly discuss satisfaction and expectations with collaborators; continually advocate for clear understanding of role.
- Develop new collaborations with high degree of supervision.
Academic Output (20%): Develop presentations, grant sections, and manuscript sections with subsequent review by peers and mentors.
- Regularly contribute to bioinformatics-focused manuscripts and publications.
- Regularly contribute to podium presentations and posters.
- Contribute to bioinformatics sections of grant and award proposals.
Job Responsibilities (Continued)
Job Responsibilities (Continued)
Required Licenses, Certifications, Registrations
Required Education and Experience
Required Education: Bachelor’s Degree in biological or computational discipline.
- At least three (3) years of experience in applied bioinformatics, genomics, and computational work.
- Experience with management and analysis of complex data types.
- Experience with genomic/proteomic data analysis methods.
Preferred Education, Experience & Cert/Lic
Preferred Education: Master’s or Doctorate Degree in biological or computational discipline
- Four (4) or more years of experience in applied bioinformatics, genomics, and computational work. This experience can be inclusive of a relevant PhD dissertation.
- Experience with Python, Perl, or other languages.
- Experience with pipeline or workflow development frameworks.
- Experience or knowledge of technologies commonly used in biological labs, such as PCR, cloning, electrophoresis gels, and cell culture.
Additional Technical Requirements
- Strong UNIX/LINUX expertise required.
- Proficiency in R or similar commonly used bioinformatics language required.
- Proficiency in various open source and commercial bioinformatics resources and software required.
- Knowledge of the working mechanism of microarray, NGS, mass spectrometry, or other high-throughput technologies and awareness of their strengths and weaknesses, as well as applicability to a specific biological problem is preferred.
- Familiarity with resources of genomic data sets and analysis tools, such as UCSC Genome Browser, Bioconductor, ENCODE, and NCBI databases is required.
- Ability to correctly select and perform statistical tests for most types of genomic data, and to properly interpret their results in the scenario of a specific study is preferred.
- Ability to interact with biologists and clinicians during a scientific discussion is required.
- Accountability and attention to timelines.
- Excellent organization and communication skills with an emphasis on strong presentation skills.
- Ability to independently plan and execute analyses of moderate complexity required.
- Ability to provide objective validation of results required.
- Ability to work in a team environment.