Computational Bioinformatics Analyst

Sanford Health•Rapid City, SD

2d•$35 - $57•Onsite

About The Position

The Computational Bioinformatics department is involved in captivating projects that span both clinical and research domains, all centered around precision medicine in patient care. These projects require collaborative work with cross-functional teams to develop a comprehensive approach to project implementation. The clinical projects involve implementing genetic testing panels that are directly applicable to patient care, while the research projects further Imagenetics’ goal of advancing education, knowledge, and research in the field of genomics. The Bioinformatics team works as data analysts, pipeline engineers and data scientists and wear multiple hats throughout. Provides support in computational analysis of or the logical and technical processes necessary for building analyses of phenome, metabolome, glycome, lipidome, infamome, transcriptome, epigenome, and genome, as well as the integration of such with electronic medical record platforms. Possesses proven academic track record of innovation in one or more of these areas. The computational and database systems developed by the application will be used to gather, generate, and track patient data and develop medically actionable hypothesis. Familiarity with security and privacy requirements applicable for working with patient data in a medical environment and in the development of appropriate ethically and legally compliant policies is desirable. Establishes data analysis plans for each project, project tracking, outcome monitoring, statistical validation and analysis. and manuscript generation. Understands bioinformatics data format, tools, and analytical approaches. Conducts gene pathway analysis. Understands data and where/how it is stored. Writes standard operating procedures (SOPs) for clinical test and writes out valid documentation. Programming for analysis of data. Develops computational services, takes genetic test results and runs quality analysis, then generates report to reporting platform to be reviewed and sign off by at the lab. Statistical validation and analysis. Assists in determining the required statistical analysis or finds and/or creates new analysis. Develops ways to analyze gene expression data picked up from publicly available sources. Identifies what algorithms best analyze the data Provide statistical and computational tools for biologically based activities, such as genetic analysis, measurement of gene expression, or gene function determination. Critically evaluate existing bioinformatics tools and statistical learning methods to best gain insights from experimental and production data, developing new methods as necessary. Ensure data integrity, traceability, accuracy, and security throughout the analysis pipeline. Implement analysis pipelines using software engineering best practices (code review, version control, automated testing) Interfaces with various stakeholders including Research and laboratory professionals. Present results in written or oral reports for computational biologists, molecular biologists, clinicians, and research collaborators. Understands best practices for data management especially large sets of genomic data. Enhance, maintain, and support established NGS pipelines and workflows for clinical applications. Analyze and visualize laboratory and other clinical data using established data analysis methods and newly developed tools

Requirements

Master’s in Bioinformatics, Biostatistics or Biomedical engineering or a Ph.D. in a related math or science field is required.
Experience in bioinformatics or closely related field is required including demonstrated experience in next generation sequence analysis.
Proven track record in analyzing, interpreting, and modeling biological data, including next-generation sequencing data, using scripting languages and statistics software packages for data manipulation, analysis, and visualization.
Experience using statistical and machine learning methods as applied to molecular biology and genomics.
Proficient in using a wide range of bioinformatics tools and resources for genomics and sequence analysis.

Nice To Haves

Familiarity with security and privacy requirements applicable for working with patient data in a medical environment and in the development of appropriate ethically and legally compliant policies is desirable.
Experience with electronic medical record (EMR) APIs and interfaces and movement of data out of and into EMRs is desirable.
Experience in programming Python, Perl, R, Ruby and Java is desirable.
Experience with the integration and management of multiple data sources, workflows, and enabling integrated management of and reporting on those data and process through graphical user interfaces is desirable.
Preference will be given to those individuals with expertise in genetic and/or epigenetic analyses and medical and/or research informatics experience.
Experience deploying automated analysis pipelines using cloud computing resources.

Responsibilities

Establishes data analysis plans for each project, project tracking, outcome monitoring, statistical validation and analysis. and manuscript generation.
Understands bioinformatics data format, tools, and analytical approaches.
Conducts gene pathway analysis.
Understands data and where/how it is stored.
Writes standard operating procedures (SOPs) for clinical test and writes out valid documentation.
Programming for analysis of data.
Develops computational services, takes genetic test results and runs quality analysis, then generates report to reporting platform to be reviewed and sign off by at the lab.
Statistical validation and analysis.
Assists in determining the required statistical analysis or finds and/or creates new analysis.
Develops ways to analyze gene expression data picked up from publicly available sources.
Identifies what algorithms best analyze the data
Provide statistical and computational tools for biologically based activities, such as genetic analysis, measurement of gene expression, or gene function determination.
Critically evaluate existing bioinformatics tools and statistical learning methods to best gain insights from experimental and production data, developing new methods as necessary.
Ensure data integrity, traceability, accuracy, and security throughout the analysis pipeline.
Implement analysis pipelines using software engineering best practices (code review, version control, automated testing)
Interfaces with various stakeholders including Research and laboratory professionals.
Present results in written or oral reports for computational biologists, molecular biologists, clinicians, and research collaborators.
Understands best practices for data management especially large sets of genomic data.
Enhance, maintain, and support established NGS pipelines and workflows for clinical applications.
Analyze and visualize laboratory and other clinical data using established data analysis methods and newly developed tools