Bioinformatics Scientist II

Children's Hospital of Philadelphia•Philadelphia, PA

26d•Hybrid

About The Position

The Genomic Diagnostic Laboratory (GDL) of the Children's Hospital of Philadelphia is seeking a highly motivated Bioinformatics Scientist II with an interest in laboratory diagnostics to join our team. This is an exciting opportunity to work collaboratively within the bioinformatics team and together with our laboratory directors, supervisors, analysts, genetic counselors, and wet bench team to develop and improve pipelines and other tools to support accurate, timely diagnostic testing that has a direct impact on establishing the diagnosis, prognosis, treatment, and management plan for our patients. The team also supports cutting edge R&D efforts including new clinical test development and clinical research. The ability to communicate in a highly matrixed environment will be key to the candidate's success. Seasoned professionals as well as new graduates are welcome to apply and will be considered for the career ladder position commensurate with their experience. This position is hybrid with weekly onsite work at our Philadelphia campus as appropriate. The Bioinformatics Scientist II is primarily focused on developing and validating frameworks and algorithms supporting clinical diagnostic tests as a bioinformatics domain expert. The scope of the work will include the development and improvement of bioinformatics workflows and processes, bioinformatics pipeline QC and troubleshooting, as well as presentation of results for multi-omics-based tests. The position allows for a wide variety of activities described within the job responsibilities. Activities occur with a moderate degree of supervision with moderate latitude for independent judgment. The ideal candidate will have experience in the following: Experience with software and algorithms used for NGS data analysis. Experience with object-oriented development. Experience with machine learning and data modeling in genomic data Experience with bioinformatics pipeline or workflow development frameworks, e.g. WDL, CWL, and Airflow. Experience in implementation of data manipulation and analysis pipelines and applications, including small variant, copy number variation and structure variation calling Knowledge on general hardware and bioinformatics-specific hardware configurations, a solid understanding of workflow language (e.g., WDL, Airflow, Snakemake), HPC job scheduler (e.g., SLURM), and genomics analyses (e.g., RNASeq, DNASeq) Experience in machine learning based data analysis on NGS data is desired Experience or knowledge of technologies commonly used in biological labs, such as PCR, cloning, electrophoresis gels, and cell culture.

Requirements

Bachelor's Degree - Required
At least three (3) years experience in applied bioinformatics, genomics, and computational work Required
Experience with management and analysis of complex data types. Required
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.
Familiarity with resources of genomic data sets and analysis tools, such as UCSC Genome Browser, Bioconductor, ENCODE, and NCBI databases is required.
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.

Nice To Haves

Master's Degree biological or computational discipline - Preferred
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
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.
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.

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.