[Remote] Lead Bioinformatics Scientist

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Note: The job is a remote job and is open to candidates in USA. Baylor Genetics is seeking an accomplished and visionary Lead Bioinformatics Scientist to advance innovation within the Bioinformatics R&D and Data Science organization. This role involves driving the design, development, and implementation of advanced computational methods and analytical pipelines to enhance genomic testing and interpretation capabilities.


Responsibilities

  • Serve as a scientific authority in bioinformatics, computational biology, statistical, and machine learning methods for genomic and clinical data analysis.
  • Contribute to the strategic bioinformatics roadmap, integrating novel algorithms, predictive modeling, and AI/ML approaches to enhance diagnostic yield, turnaround time, and interpretability.
  • Act as a subject matter expert (SME) in computational genomics, variant annotation, and clinical data integration.
  • Translate research innovations into robust, production-ready tools and pipelines that meet clinical and regulatory requirements.
  • Drive cross-functional collaborations to deliver scalable, interpretable, and validated computational solutions.
  • Design, develop, and optimize bioinformatics methods and pipelines for secondary (alignment, variant calling) and tertiary (annotation, prioritization, interpretation) analysis.
  • Implement, validate, and maintain workflows using Nextflow, Snakemake, or similar orchestration tools for reproducible, scalable analysis.
  • Develop and evaluate computational, statistical, and machine learning models for variant classification, pathogenicity prediction, and genotype–phenotype correlation.
  • Integrate multi-omics, phenotypic, and clinical datasets to improve analytical accuracy and discovery power.
  • Ensure computational reproducibility, scalability, and maintainability through best practices in software engineering and CI/CD.
  • Support clinical validation of new and developed tools and pipelines, ensuring compliance with CLIA, CAP, and related quality standards.
  • Lead investigative projects to develop novel computational frameworks and analytical methodologies for genomic discovery and clinical interpretation.
  • Apply and evaluate computational, statistical, ML, and AI-based methods to address key challenges in variant annotation, classification, and reporting.
  • Design and execute benchmarking studies to evaluate new algorithms, annotation resources, and models.
  • Contribute to the scientific community through publications, conference presentations, and collaborations.
  • Employ advanced computational and statistical techniques to extract biological insights from genomic and clinical data.
  • Use regression, probabilistic, and predictive models to improve variant quality metrics, scoring, and prioritization.
  • Collaborate with data scientists and engineers to integrate ML/AI methods into clinical-grade pipelines.
  • Utilize effective data visualization and interpretability frameworks to communicate findings to scientific and clinical audiences.
  • Partner with clinical geneticists, molecular scientists, software engineers, and data scientists to translate R&D innovations into clinical deployment.
  • Act as a bridge between bioinformatics R&D and clinical operations, ensuring analytical rigor and compliance with regulatory standards.
  • Communicate technical strategies and results clearly to leadership and cross-functional stakeholders.

Skills

  • Master's or higher degree (PhD preferred) in Bioinformatics, Computational Biology, Genomics, Computer Science, Genomic Data Science, or related quantitative field.
  • 8+ years of professional experience in bioinformatics, computational genomics, data science, or genomic R&D, including 3–5 years in a principal or leadership role.
  • Proven expertise in pipeline development, algorithm design, and computational genomics research.
  • Hands-on experience in secondary and tertiary genomic analysis.
  • Demonstrated integration of statistical and data science approaches in genomics applications.
  • Proficiency in Python, R, and at least one compiled language (C/C++, Java, or similar).
  • Expertise in NGS data formats and tools.
  • Strong knowledge of clinical genomic databases and annotation resources.
  • Solid foundation in statistical modeling, data analysis, and feature engineering for biological data.
  • Familiarity with machine learning frameworks (TensorFlow, PyTorch, Scikit-learn, etc.) applied to genomic data.
  • Experience with workflow orchestration tools (Nextflow, Snakemake, Cromwell) and cloud-based computing (Azure, AWS, GCP).
  • Experienced with data management, version control (Git), and CI/CD best practices.
  • Knowledge of multi-omics data integration and modern visualization techniques.
  • Strong scientific reasoning and analytical problem-solving skills.
  • Proven ability to lead R&D initiatives from concept through validation and deployment.
  • Deep understanding of genomic data, algorithms, and biological context.
  • Excellent written and verbal communication for technical and clinical translation.
  • Collaborative mindset and ability to work across disciplines.
  • Commitment to innovation, quality, and patient-centered outcomes.
  • Experience working in a clinical genomics or regulated diagnostic environment strongly preferred.

Company Overview

  • Baylor Genetics offers a full spectrum of cost-effective, genetic testing, and provides clinically relevant solutions. It was founded in 1978, and is headquartered in Houston, Texas, USA, with a workforce of 501-1000 employees. Its website is https://www.baylorgenetics.com/.

    • Company H1B Sponsorship

  • Baylor Genetics has a track record of offering H1B sponsorships, with 3 in 2025, 1 in 2024, 3 in 2023, 1 in 2022, 1 in 2021, 3 in 2020. Please note that this does not guarantee sponsorship for this specific role.

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