Bioinformatics Scientist II

Req ID: 5361

Shift: Days

Employment Status: AF – Active – Regular – Full Time

Job Summary

The Bioinformatics Scientist II will apply knowledge of bioinformatics algorithms and analysis platforms to consult with investigators on their projects and to serve as a collaborative investigator in academic output including assistance in experimental design and writing, including creation of scientifically rigorous visualizations, communications, and presentations of results. The Scientist will be working with a diverse, highly skilled, team-oriented laboratory and clinical oncology team in the Maris Lab at CHOP. The Candidate will process sequencing data both in local clusters as well as through cloud services. Candidates must be able to show proficiency in development of analysis systems, such as pipelines and automation for analysis of high-throughput data. Candidates will have an excellent understanding of the strengths and limitations of commonly used data generating platforms as they apply to experimental endpoints and will readily be able to set expectations and defend results through frequent and direct communication with collaborators.

Job Responsibilities

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

None

Required Education and Experience

• MS or Ph.D. In a biological, computational or related discipline.
• At least three to five (3-5) years experience in applied bioinformatics, genomics and computational work.
• Working experience with next generation sequencing data using common tools, including BWA, Novoalign, STAR, GATK, freebayes, samtools, Picard, SnpEff, bedtools, tabix, and other tools and resources, e.g. sequence retrieval, alignment and clustering techniques, expression profiling and protein related analysis, using major databases and data standards in the field.
• Working knowledge and deep understanding of algorithms, data structures, and scientific programming, including workflow management packages
• Proficiency with the minimum programming languages of Python, Perl, and R.
• Pipeline development, analysis, and troubleshooting experience with multi-omics data including whole genome and exome, RNA-Seq, targeted sequencing, ChIP-Seq, ATAC-Seq, SNP and methylation arrays
• Proficiency in Unix/Linux operating systems.
• Ability to work efficiently on multiple projects.
• Strong working knowledge of statistics including but not limited to hypothesis testing tools, power analysis, and the ability to aid researchers in experimental design
• Ability to communicate scientific and informatics concepts to a wide range of audiences.
• Ability to work independently with minimal guidance while consistently exercising good judgment.
• Ability to work in a academic team environment
• Excellent interpersonal skills to interact with both research and technical staff.
• Familiarity with repositories of genomic data sets and analysis tools, such as UCSC Genome Browser, Bioconductor, ENCODE, and NCBI databases.

Preferred Education, Experience & Cert/Lic

• Experience in high performance computing and cluster environments and cloud computing is highly desirable.
• Knowledge and biological understanding of common library preparation technologies, including those from CGI, Illumina, and LifeTechnologies
• Ability to independently research different analysis tools and parameters within each to come up with the most appropriate tool and workflow for answering relevant biological questions (e.g.: testing multiple algorithms for sensitivity and specificity)
• Prior experience in oncology is very desirable but not required.
• Strong working knowledge or training in advanced biomedical research.
• Knowledge of technologies commonly used in biological labs, such as PCR, cloning, electrophoresis gels, and cell culture.
• Familiarity with broadly used machine learning algorithms

Additional Technical Requirements

Accountability and attention to timelines.

Excellent organization and communication skills with an emphasis on strong presentation skills.

Ability to work in a team environment.