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.
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