Bioinformatician III- Public Health Sciences

Henry Ford Health•Detroit, MI

About The Position

As a Bioinformatician III, you will work independently with investigators and clinicians to design, collect, process, and analyze complex biomedical data across domains such as genomics, transcriptomics, imaging, and electronic health records. Your expertise will drive the development of analytic pipelines, provide in-depth data analysis, and support the integration of multi-domain data to advance research and clinical outcomes.

Requirements

Ph.D. in bioinformatics, computational biology, biostatistics, statistics, genetics, computer science, or a related quantitative or biomedical field.
Strong computational and analytical skills with demonstrated experience in processing and analyzing complex biomedical or biological data.
Experience using statistical or bioinformatics software, such as R, Bioconductor, or comparable tools, with proficiency in multiple programming or scripting languages, such as Python, R, C, Java, Groovy, or Bash.
Experience working from the command-line in a high-performance-computing Linux environment, with extensive experience running processes in parallel and management of compute and memory resources to maximize efficiency.
Experience with biomedical image analysis, batch image processing, computer vision, machine learning-based image analysis, or closely related methods that can be applied to histopathologic and other biomedical imaging data.
Foundational knowledge of statistical methods and their application to biological or biomedical research.
Demonstrated scientific contributions through peer-reviewed manuscripts, abstracts, presentations, software, analytic workflows, or publicly available code.
Bachelor's degree in genetics, biology, biostatistics, bioinformatics, or related field with 7 years of experience, or Master's degree with 5 years of experience, or Ph.D. with relevant experience.
Experience with statistical/bioinformatics software (e.g., R, Bioconductor, SAS) and programming languages (e.g., Python, C, Java, shell scripting).
Knowledge of Linux and command line environments.
Excellent verbal and written communication skills.

Nice To Haves

Experience developing, automating, troubleshooting, and documenting reproducible computational workflows.
Experience analyzing multi-omics data, including genomics, transcriptomics, single-cell sequencing, spatial transcriptomics, or related technologies.
Experience applying machine learning or artificial intelligence methods to biomedical data, including convolutional neural networks or related image-analysis approaches.
Experience developing or managing workflows with tools such as Nextflow, Snakemake, or comparable workflow management platforms.
Experience using version-control systems like GitHub and sharing well-documented code to support transparent and reproducible research.

Responsibilities

Design and implement analytic pipelines for processing and analyzing biomedical data.
Provide in-depth analysis of studies using computational and statistical methods.
Integrate data across multiple domains and time points to support research objectives.
Develop and document new approaches for bioinformatics analysis.
Oversee the work of other bioinformaticians and data processes.
Prepare interpretive reports and contribute to scientific publications and grant applications.
Collaborate directly with investigators, clinicians, and multidisciplinary research teams to perform research involving the analysis of genomic, transcriptomic, single-cell, spatial transcriptomic, imaging, clinical, and other biomedical data types.
Design, implement, optimize, automate, and maintain scalable, reproducible, and well-documented analytic pipelines for complex biomedical data using appropriate programming languages, workflow-management tools, and version-control practices.
Develop computational approaches for the batch processing and analysis of biomedical images, including histopathologic and medical imaging data.
Use high-performance computing environments (CPU- and GPU-based) to process and analyze large and complex datasets.
Prepare clear interpretive reports, visualizations, and presentations for technical and nontechnical audiences.
Promote reproducible research through clear documentation of analytic decisions, methods, workflows, and code.