Req ID: 113752
Shift: Days
Employment Status: Regular – Full Time
Job Summary
The Bioinformatics Unit (BIXU) within the Center for Data Driven Discovery (D3b) at The Children’s Hospital of Philadelphia (CHOP) is seeking a level II Bioinformatics Scientist to join our over 30 professional data engineers, developers, and bioinformatics scientists committed to accelerating cures for children with cancer. We’re looking for creative problem solvers who can leverage software and system engineering to perform both upstream and downstream large-scale genomic analysis. You will be involved in brain tumor research and clinical trials projects characterized with multiple next-generation sequencing or microarray technologies (WGS, WXS, Targeted Panel, RNA-Seq, miRNA, methylation, proteomics, scRNA-Seq). The primary goal will be to support emerging radiogenomics projects and data integration efforts that drive D3b’s research projects. This position is an entry level position for a PhD-level professional and a 2nd stage position for those with moderate experience without a PhD. Activities occur with a moderate degree of supervision with some latitude for independent judgment, development of bioinformatics workflows and processes, and presentation of results. The Bioinformatics Scientist II is primarily focused on supporting scientific teams and projects as a bioinformatics domain expert.
The Bioinformatics Scientist will attend project meetings and interact daily with BIXU 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.
Long Description
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, machine learning, and at least five years of experience (inclusive of focused academic training) in bioinformatics projects utilizing bash and either Python or R programming.
The successful candidate must have experience in the following areas:
a) Experience in the analysis of sequencing data related to cancer (e.g. somatic single nucleotide variants (SNVs), indels, structural variations (SVs), fusions, RNA expression data, and copy number analyses).
b) Experience in setting up, implementing, and validating/testing machine learning models for cancer biomarker or therapeutic target discovery.
Additionally, the successful candidate must have experience in at least two of the following areas:
c) Experience setting up pipelines and working in high-performance and/or cloud-based computing environments towards bioinformatics data processing for large-scale projects.
d) 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.
e) Code organization and reproducible workflow experience using git, docker, and R or Python notebooks.
The successful candidate will be expected to implement his/her machine learning expertise within the context of predicting potential therapeutic modalities within pediatric brain tumors.
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, code, and research transparency and reproducibility.
Job Responsibilities
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.
Develop analysis plans that enable integrative analyses of imaging and genomic data.
Develop at least one “specialty” analytical or biomedical area that serves the collaborative team.
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.
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.
Required Education and Experience
Required Education: Bachelor’s Degree in biological or computational discipline.
Required Experience:
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
Preferred Experience:
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.
All CHOP employees who work in a patient building or who provide patient care are required to receive an annual influenza vaccine unless they are granted a medical or religious exemption.
Children’s Hospital of Philadelphia is committed to providing a safe and healthy environment for its patients, family members, visitors and employees. In an effort to achieve this goal, employment at Children’s Hospital of Philadelphia, other than for positions with regularly scheduled hours in New Jersey, is contingent upon an attestation that the job applicant does not use tobacco products.
Children’s Hospital of Philadelphia is an equal opportunity employer. We do not discriminate on the basis of race, color, gender, gender identity, sexual orientation, age, religion, national or ethnic origin, disability or protected veteran status.
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CHOP Careers Contact
Talent Acquisition
2716 South Street, 6th Floor
Philadelphia, PA 19146
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