[Remote] Lead Bioinformatics Scientist

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 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. Apply tot his job