Cell Therapy Bioinformatics Scientist II

SHIFT:

• 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.

• Bachelor's Degree Required
• Master's Degree biological or computational discipline Preferred

• At least three (3) years experience in applied bioinformatics, genomics, and computational work Required
• Experience with management and analysis of complex data types. Required
• At least four (4) years of experience in applied bioinformatics, genomics, and computational work Preferred
• Experience with Python, Perl, or other languages Preferred
• Experience with pipeline or workflow development frameworks Preferred
• Experience or knowledge of technologies commonly used in biological labs, such as PCR, cloning, electrophoresis gels, and cell culture Preferred

• 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.