Principal Bioinformatics Scientist

Baylor Genetics•, TX

About The Position

Baylor Genetics is seeking an accomplished Principal Bioinformatics Scientist to join the Bioinformatics R&D and Data Science organization. This individual will serve as a senior technical expert and key individual contributor responsible for developing, optimizing, and maintaining the computational methods and pipelines that power Baylor Genetics’ genomic testing and interpretation platforms. The Principal Bioinformatics Scientist will drive research and development efforts across secondary and tertiary analysis, variant interpretation, and computational genomics, while also providing technical expertise and production support for Baylor Genetics’ operational bioinformatics systems. The ideal candidate combines deep expertise in genomics, bioinformatics, and computational modeling with strong practical skills in data science, pipeline development, and system optimization in a clinical genomics environment. This role bridges R&D innovation and production support, ensuring that novel research solutions are validated, robust, and seamlessly integrated into clinical operations under CLIA, CAP, and other regulatory standards.

Requirements

Master’s or higher degree (PhD preferred) in Bioinformatics, Computational Biology, Genomics, Computer Science, or a related quantitative field.
6+ years of professional experience in bioinformatics, computational genomics, or computational biology, including extensive hands-on work with large-scale NGS datasets.
Proven experience developing and maintaining bioinformatics pipelines in both R&D and production settings.
Strong record of integrating computational, statistical, and/or machine learning methods into genomics applications.
Proficiency in Python and R; familiarity with one or more compiled languages (C/C++, Java, or similar).
Strong experience with NGS data formats and tools.
Expertise with workflow orchestration frameworks (Nextflow, Snakemake, Cromwell).
Deep knowledge of genomic databases.
Familiarity with cloud computing (Azure, AWS, GCP), containerization (Docker/Kubernetes), and version control (Git).
Strong scientific reasoning and analytical problem-solving abilities.
Hands-on approach to both R&D innovation and operational troubleshooting.
Ability to collaborate effectively with multidisciplinary teams.
Excellent written and verbal communication skills.
Commitment to quality, compliance, and scientific excellence in a clinical setting.

Nice To Haves

Experience working in a clinical genomics or regulated diagnostic environment strongly preferred.
Demonstrated success collaborating with clinical or laboratory teams to troubleshoot and improve production systems.
Background in statistical modeling, data analysis, and machine learning frameworks (e.g., Scikit-learn, TensorFlow, PyTorch) preferred.
Knowledge of multi-omics data integration and data visualization methods a plus.

Responsibilities

Design, develop, and optimize computational workflows and pipelines for secondar and tertiary genomic analyses.
Implement reproducible and scalable bioinformatics workflows using Nextflow, Snakemake, or similar orchestration frameworks.
Develop, evaluate, and apply algorithms and statistical or machine learning models for variant detection, classification, and genotype–phenotype correlation.
Integrate multi-omics, phenotypic, and clinical datasets to enhance analytical accuracy and interpretability.
Translate R&D innovations into production-ready tools that improve diagnostic accuracy, speed, and reproducibility.
Provide ongoing support and maintenance for bioinformatics systems and pipelines, ensuring operational stability, accuracy, and efficiency.
Actively collaborate with clinical and laboratory teams to investigate and resolve pipeline bugs, data inconsistencies, and performance issues.
Participate in root-cause analyses and implement sustainable solutions to ensure reliable clinical operations.
Work closely with clinical stakeholders to define, design, and deploy enhancements and new system capabilities based on user feedback and evolving clinical requirements.
Ensure all production updates and improvements comply with regulatory and quality standards (CLIA, CAP, etc.) and are appropriately documented and validated.
Support the design and validation of new computational tools and pipelines according to clinical genomic standards and regulatory requirements.
Conduct analytical validation, benchmarking, and verification of newly developed algorithms and workflows.
Collaborate with quality, laboratory, and clinical teams to ensure smooth transition of tools from R&D to production.
Lead exploratory projects to develop and assess novel computational or statistical methods for variant classification, annotation, and interpretation.
Perform benchmarking studies to evaluate emerging algorithms, annotation resources, and data integration approaches.
Contribute to internal documentation, publications, and presentations highlighting R&D advancements.
Collaborate closely with data scientists, software engineers, and clinical scientists on cross-functional R&D and production initiatives.
Provide technical mentorship and peer review for bioinformatics scientists and analysts.
Promote best practices in scientific computing, code reproducibility, and analytical rigor.
Maintain clear, version-controlled documentation of all analytical pipelines and validation processes.
Contribute to continuous improvement of computational infrastructure, software practices, and data management.
Stay current with evolving trends, standards, and technologies in computational genomics and clinical bioinformatics.