Senior Bioinformaticist

The University of Texas at Arlington Portal-posted 1 day ago
Full-time • Mid Level
Onsite • Arlington, TX
5,001-10,000 employees
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The Senior Bioinformaticist will lead the development and strategic implementation of advanced bioinformatics solutions within the joint UTA–Cook Children’s precision medicine and genomic research ecosystem. This position will develop the strategy, manage pipeline architecture, and drive innovation in genomic data integration. The candidate will serve as a senior architect bridging population genetics, clinical variant interpretation, and translational neuroscience within precision neurology programs. The position will work within UTA’s Center for Innovation in Health Informatics ( CIHI ) and collaborate closely with Cook Children’s variant scientists, genetic counselors, clinicians, data scientists, and computational teams.

  • Pipeline Architecture and Optimization Design and oversee enterprise-scale genomic pipelines for SNV /indel calling, structural variant analysis, annotation, and QC.
  • Implement cloud- and HPC -based workflows for large-scale genomic datasets.
  • Establish best practices for data governance, security, IRB , and PHI protection standards across clinical and research environments.
  • Lead design and maintenance of end-to-end germline and somatic WES / WGS pipelines, including rare variants, CNV , mosaicism, and gene-based burden analyses.
  • Optimize pipelines for consortium-scale studies (>100k) using HPC and cloud environments.
  • Strategic Variant Interpretation Leadership Lead innovative approaches for complex gene-specific variant curation, integrating multiple annotation resources (gnomAD, ClinVar, HGMD , UniProt).
  • Serve as subject matter expert for the Cook–UTA variant review boards, providing expert level variant summaries and pathogenicity assessments.
  • Lead development ACMG / AMP -aligned re-analysis pipelines.
  • Data Integration & Knowledgebase Strategy Architect for scalable, harmonized, searchable structures that integrate genomic, clinical, and experimental/functional datasets.
  • Drive development of UTA–Cook knowledgebases and web-facing tools that support clinical genetics and translational research for precision medicine programs.
  • Advanced Analytics and Machine Learning Lead ML/AI approaches for pathogenicity prediction, phenotype clustering, or multimodal data modeling.
  • Publish findings and contribute to grant proposals, translational informatics studies in precision neurology, and peer reviewed manuscripts.
  • Software Development and Visualization Oversee creation of R/Shiny, Python, or JavaScript-based applications for interactive variant exploration or phenotype dashboards.
  • Ensure usability and scalability of visualizations for clinical teams, publications, and presentations.
  • Clinical Informatics & Decision Support Leadership Partner with Cook IT, Epic analysts, and UTA computational teams to operationalize unstructured data into structured formats using ontologies and NLP -based approaches.
  • Guide clinical decision-support tool development for precision neurology and rare disease programs.
  • Mentorship and Program Development Represent UTA -Cook partnership in strategic planning and external collaborations as well as with variant scientists, genetic counselors, bioinformaticians, researchers, and clinical partners.
  • Mentor students and junior staff; Collaborate with Lead Faculty to develop training programs in Genome-Informed Care.
  • Other Duties as Assigned
  • Master’s degree in Bioinformatics, Computational Biology, Genetics, Data Science, Computer Science, or related field.
  • Five (5) years of experience in genomic data processing, variant annotation, or large-scale data analysis.
  • Proven leadership in pipeline development, variant interpretation, and clinical informatics.
  • PhD in Bioinformatics, Genetics, Computational Biology, or a related quantitative field.
  • Demonstrated success in integrating large multimodal datasets (genomic, phenotypic, functional).
  • Experience leading VCF / FASTQ processing, variant interpretation frameworks, and cloud-based deployments.
  • Experience leading clinical genetics programs or variant scientists.
  • Experience leading with machine learning, predictive modeling, or structure-informed variant analysis.
  • Track record of high-impact publications and active participation in international consortia.
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