The Department of Biomedical and Health Informatics at The Children’s Hospital of Philadelphia is currently searching for a well-qualified Bioinformatics Scientist III, which is a senior-level contributor position (5-10 years direct experience).
CHOP is a fantastic place to work offering excellent benefits and strong support of professional development. Our Bioinformatics group (BiG) operates in the Department of Biomedical and Health Informatics (DBHI), a multifaceted informatics department comprising application development, big data analytics, education, clinical informatics and scientific computing. The BiG has a diverse and knowledgeable group of bioinformaticians with over a hundred person-years of experience and offers a supportive and collegial working environment.
The salaries for the Bioinformatics Scientist III are very competitive (starting over 100K).
We are integrated with academic research at the University of Pennsylvania. Also, many of the PIs we partner with are faculty at UPenn as well as other institutions.
BiG bioinformaticians are valued researchers in their own right.
Last, year, BiG bioinformaticians were listed as authors on 47 papers, seven of which were first-author and several featured in high-impact journals.
More info at DBHI is here: http://dbhi.chop.edu/
The Bioinformatics Scientist III will apply knowledge of bioinformatics algorithms and analysis platforms to consult with investigators on their projects and to serve as a collaborative and sometimes lead 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 laboratory and clinical oncology team, therefore previous knowledge of clinical oncology is highly desirable. This individual will work in a highly skilled, team-oriented environment applying knowledge of cellular, molecular, and computational biology to create novel methods and testable scientific hypotheses. The Candidate will be processing data both in local clusters as well as through cloud services. Candidates must be able to show proficiency in analysis system development, 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.
MS or Ph.D. In a biological, computational or related discipline
Five to ten (5-10) 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.
Experience in the development, enhancement and automation of methods for the analysis of high-dimensional data including whole-genome, whole-exome, and RNA sequencing data.
Experience in high performance computing and cluster environment. Previous experience in cloud computing is highly desirable. Familiarity with repositories of genomic data sets and analysis tools, such as UCSC Genome Browser, Bioconductor, ENCODE, and NCBI databases Proficiency in Unix/Linux operating systems. Prior experience in oncology is very desirable but not required Strong working knowledge or training in advanced biomedical research. Ability to work efficiently on multiple projects. Ability to choose the correct statistical tests for the problem and help researchers in experimental design. Ability to communicate scientific and informatics concepts to a wide range of audiences. Knowledge of technologies commonly used in biological labs, such as PCR, cloning, electrophoresis gels, and cell culture. Excellent interpersonal skills to interact with both research and technical staff. Ability to work independently with minimal guidance while consistently exercising good judgment. Ability to work in a academic team environment.