Bioinformatics Scientist

Responsibilities

• Independently design analytical strategies; identify and resolve scientific or technical challenges; design studies; analyze and interpret high-throughput biological data; and prepare and present results.
• Develop work products and documentation supporting integrative analysis of internally generated datasets.
• Contribute to manuscript preparation by drafting data analysis sections, creating publication-quality figures, and facilitating data submission to appropriate external repositories.
• Develop and maintain custom analytical software, scripts, and computational pipelines.
• Evaluate and implement commercial and open-source tools; lead pipeline design and development efforts; establish reproducible workflows and standard operating procedures (SOPs); and provide comprehensive methodological documentation.
• Develop and optimize analytical pipelines for multi-omics data.
• Identify, evaluate, and implement appropriate computational methods for preprocessing, quality control, integration, clustering, differential expression, and downstream analysis of single-cell datasets.
• Provide ad-hoc training sessions and hands-on workshops on bioinformatics tools and analytical methodologies.
• Participate in group meetings; present analytical findings; and maintain current knowledge of emerging technologies and advances in biology, statistics, computer science, and bioinformatics.
• Collaborate with scientists to design, analyze, manage and interpret all types of data.
• Work with an interdisciplinary team to apply computational data analysis approaches to make biological discoveries.
• Conduct analyses on NextGen sequencing data including data derived in ChIP-Seq, RNA-Seq, miRNA-Seq and other experimental models.
• Conduct analysis and interpretation on data from genomic platforms including expression, exon, tiling, promoter and other array types.
• Work independently with research groups to develop, implement, and refine analytical pipelines for high throughput genomics, transcriptomic, epigenetic, proteomics, digital spatial and other omics data.
• Generate and optimize programs and scripts for the analysis of data; create programs and algorithms and develop computational infrastructure resources for organizing and parsing data from large and complex data.
• Serve as bioinformatics expert and coordinate with teams of biologists to conduct experimental queries and/or perform portions of studies using complex procedures and techniques common to modern bioinformatics.
• Coordinate building bioinformatics infrastructure to ensure easy and meaningful scientific analysis and interpretation of data.
• Provide broad-based programming and analytic support for a wide variety of bioinformatic and research projects.
• Install, troubleshoot and run open-source and commercial scientific software on platforms.
• Perform computations on research data analysis.
• Perform computational analysis of, and interpret results.
• Provide reports based on analysis of scientific data.
• Perform sequencing and alignment of raw data, and interpret new data using larger public access datasets.
• Provide interpretive analyses of data derived from different experimental platforms to generate biological meaning.
• Write custom programs and algorithms to support data analyses and discovery.
• Apply machine learning approach to analyze and integrate large-scale, complex multi-omic data to identify biological insights.
• Work with staff on scientific programming and experimental design.
• Design and execute computational experiments.
• Work with staff on planning of experiments, and data analysis for internal and collaborative projects; use bioinformatics expertise to advise and help bench scientists on experimental design and troubleshooting.
• Work with staff to develop specifications for new analysis; design, test and implement solutions.
• Make recommendations to investigators about the correct computational tools for testing scientific hypotheses and reaching valid conclusions.
• Record observations and report results at weekly laboratory meetings.
• Maintain proper and detailed documentation of the analysis performed and report results at lab meetings.
• Attend scientific and programming meetings; take and compile comprehensive notes; organize and edit content of meeting reports.
• Prepare scientific reports and progress reports; assemble data to prepare tables, graphs and slides; conduct scientific and program related information searches and report results.
• Organize daily laboratory notebook on experiments; prepare weekly updates on on-going experiments and tasks; provide monthly progress notes on assigned projects; submit final progress reports and future directions on projects.

Requirements

• Three years of experience with a Ph.D. in Bioinformatics, Biostatistics, Computational Biology or a biological/life science (Ph.D. preferred; Master's acceptable).
• Demonstrated training or experience in machine learning, artificial intelligence, or advanced statistical modeling applied to high-dimensional biological data.
• Proficiency in R, Python, MATLAB, Linux, Unix, Java, Shell, PERL, Bash, SAS, Git, Docker/Singularity/Apptainer, workflow management tools.
• Experience with next-generation sequencing data analysis (Bulk RNA-Seq, Single Cell RNA-Seq), genome-wide association studies, microarray data analysis, pipeline development, multi-omics analysis, machine learning, cloud platforms, reproducible research practices, data visualization, representation learning for omics data, and multi-omics integration using deep learning.

Nice-to-Haves

• Field of study in Mathematics and Computer Science, Biology, Environmental Science, Genetics, or Molecular Biology.