We are looking for a highly motivated and creative bioinformatics research scientist to develop and apply innovative analytical approaches to understand the genetic modifications that drive the development and response to therapy of blood tissues. The scientist will contribute ideas to implement automate and improve existing analysis methods, assist with establishing and documenting protocols or best practices for common research tasks, develop and deploy state-of-art computational infrastructure, analytical pipelines, and conduct broad, multi-omics deep genomic analysis of experimental models and for cancers. We provide a highly interactive environment with collaborative opportunities across basic and clinical departments, access to high performance computing clusters, cloud computing environment, innovative visualization tools, highly automated analytical pipelines and mentorship from scientists with deep experience in data analysis, data management and delivery of high-quality results for highly competitive projects.
The successful applicant will have an unrivalled opportunity to engage in analysis of data generated by genomic, transcriptomic, epigenomic and single cell approaches. Specific responsibilities may involve developing and evaluating analytic tools of sequencing data, data visualization, statistical approaches for genomic analysis and manuscript preparation.
Candidates with a strong background in cancer genomics, genomics, bioinformatics, or related fields are highly encouraged to apply for this position. Excellent communication skills are essential.
You will work closely with team members of different departments and receive mentoring support, building the team, data management and analysis and delivery of high-quality results.
Your duties will include:
1. Collaborate with scientists generating sequencing data at our lab.
2. Work with short and long read technologies to develop novel variant calling methods for challenging variant types and/or difficult yet medically important regions of the genome
3. Analyze population-scale sequencing data to benchmark and improve variant callers
4. Statistical and population genetics analysis of large datasets
5. Presentation of research findings internally and externally through presentations and manuscripts
6. Collaborate with assay, clinical and computational scientists at Illumina/MGI/BGI and external research or clinical laboratories to deliver complete products.
7. You will analyze various genomic data generated at our lab using software tools within our team and by generating their own statistical analysis code. You will collaborate with oncologists, computational biologists and research scientists to study alterations in patients’ genomes in order to assist in finding new treatments and cures and so forth.
8. Works in teams with oncologists, research scientists and molecular biologists to define and execute computational approaches.
9. Performs computational analysis of next generation sequencing data, somatic copy number alterations, expression profiling, and biological pathways using in house algorithms.
10. Generates own code to conduct various other statistical analyses including those to discover relationships between cancer alterations and clinical correlates.
11. Presents data at project team meetings.
12. Assists in drafting of scientific manuscripts.
13. You will interact closely with clinical development teams and other scientists in the areas of biomarkers, bioinformatics, and research discovery departments, including Cancer Immunotherapy, Molecular Oncology, Immunology, Metabolic Disease, and Neuroscience.
A Master’s degree or higher in Biology, Bioinformatics, Computer Science or a related scientiﬁc ﬁeld, and at least 5 years bioinformatics experience.
PhD in bioinformatics, statistics, computer science, genetics or related field of study
PhD in statistical/human genetics or bioinformatics/computational biology.
B.S./B.A. in the life sciences, physical sciences, or computational sciences.
Some coding knowledge in (Python, Java, C/C++, perl or other programming/scripting languages) under linux/unix environment is required.
Experience with and the ability to deal with a wide range of users is required.
Experience of independent data analyses and project management is required.
The ability to work in teams, excellent communication and data presentation skills.
Expertise in the analysis of population genetics data, with experience in non-model systems, is preferred.
The ability to explain complex scientiﬁc and computational ideas to a wide range of scientists and researchers (such as graduate students, postdocs, and faculty), including those with little or no computational experience, will be extremely important for the successful candidate.
Experience in computational or statistical sciences or bioinformatics with a strong emphasis on computational methods for high-throughput genomic and genetic data analysis highly preferred.
Experience in managing, processing, analyzing, and interpreting data generated from one or more next-generation sequencing technologies such as whole genome sequencing, exome sequencing, RNA-Seq, single cell sequencing, ATAC-Seq, ChIPSeq.
Expertise with publicly and commercially available bioinformatics tools for next-generation sequencing analysis and genetics and genomics databases.
Significant programming experience in the use of a high-level programming language such as Perl, Python and R. Familiarity working in Linux and high-performance cluster computing environment.
Knowledge of molecular biology and experience in biologic interpretation of human genetic data are a plus.
Strong communication and interpersonal skills with the ability to present and explain findings to a diverse audience.
Good publication record in top-tier peer-reviewed journals.
Highly motivated and demonstrated ability to work both independently and collaboratively, and to handle several concurrent, fast-paced projects.
Background in molecular genetic data. Understanding of statistical methods and working in cloud computing and UNIX environments.
Experience analyzing next-generation sequencing data
Strong understanding of standard statistical methods
Strong communication skills and ability to describe complex analysis to a wide audience
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