Ngs Data Analysis Jobs in Michigan (NOW HIRING)

At Henry Ford Health, we're committed to advancing health and improving lives for the millions of people we serve across Michigan and around the world. As one of the nation's leading academic health systems, we provide a comprehensive continuum of care that includes primary and preventive services, specialty and complex care, virtual care, pharmacy, home health, eye care, health insurance, and more. With 12 hospitals and hundreds of ambulatory care locations, including former Ascension Southeast Michigan and Flint Region facilities, our growing network expands access to exceptional care in the communities we serve.
Headquartered in Detroit, Henry Ford Health is helping shape the future of healthcare through the transformative Future of Health: Detroit initiative, a $3 billion investment that is redefining our academic healthcare campus and advancing innovation, research, education, and community impact.
Our work is grounded in purpose, collaboration, and belonging. We empower team members to grow their careers, contribute innovative ideas, and make a meaningful difference every day. Whether you're caring for patients, supporting operations, conducting research, or driving new solutions, you'll be part of a team united by a shared mission: delivering exceptional care, advancing health outcomes, and building healthier communities for all.

We are seeking a detail-oriented and analytical Bioinformatician II to join our Biorepository Core Research team in Detroit, United States. In this mid-level position, you will play a critical role in supporting our research initiatives by managing, analyzing, and interpreting complex biological datasets. You will work collaboratively with researchers and laboratory staff to ensure the integrity and accessibility of our biorepository data while advancing our research capabilities through sophisticated bioinformatics solutions.

• Perform comprehensive bioinformatics analyses on next-generation sequencing (NGS) data, including quality control, alignment, variant calling, and annotation
• Develop and maintain efficient data management systems and workflows for biorepository samples and associated metadata
• Design and implement automated bioinformatics pipelines using scripting languages to streamline data processing and analysis
• Collaborate with research scientists and laboratory personnel to understand project requirements and deliver tailored analytical solutions
• Ensure data integrity, security, and compliance with regulatory standards for biorepository operations
• Troubleshoot technical issues and optimize bioinformatics tools and databases for improved performance and reliability
• Document methodologies, protocols, and analysis results with precision to support reproducibility and knowledge sharing
• Provide technical support and training to research staff on bioinformatics tools and data interpretation
• Stay current with emerging bioinformatics technologies and best practices to continuously enhance core facility capabilities
• Contribute to research publications and presentations by providing rigorous data analysis and interpretation

**Required Qualifications:**

• 3-5 years of professional experience in bioinformatics or computational biology
• Proficiency in programming languages such as Python, R, or Perl for data analysis and automation
• Strong experience with NGS data analysis and genomic data processing
• Demonstrated expertise in Linux/Unix command-line environments
• Experience with version control systems (Git) and collaborative development practices
• Solid understanding of statistical methods and their application to biological data
• Experience with relational and non-relational databases for data management
• Excellent organizational skills with the ability to manage multiple projects simultaneously
• Strong written and verbal communication skills for documenting and presenting technical findings

**Preferred Qualifications:**

• Experience working in a biorepository or research core facility environment
• Knowledge of machine learning techniques applied to biological datasets
• Familiarity with cloud computing platforms and high-performance computing environments
• Experience with workflow management systems (e.g., Nextflow, Snakemake, WDL)
• Background in genomics, molecular biology, or biostatistics
• Experience with containerization technologies (Docker, Singularity)
• Familiarity with bioinformatics databases and annotation tools (e.g., ANNOVAR, VEP, dbSNP)