Lead Bioinformatics Scientist

Baylor Genetics is seeking an accomplished and visionary Lead Bioinformatics Scientist to advance innovation within the Bioinformatics R&D and Data Science organization. This individual will serve as a scientific and technical leader, driving the design, development, and implementation of advanced computational methods, algorithms, and analytical pipelines to enhance Baylor Genetics' genomic testing and interpretation capabilities. The Lead Bioinformatics Scientist will play a central role in bioinformatics research and development, focusing on computational genomics applications, pipeline development, secondary and tertiary analysis, variant prioritization and interpretation, clinical application, and so forth. The successful candidate will combine deep scientific expertise in genomics, bioinformatics, and computational modeling with strong technical skills in data science, statistics, and algorithm design, developing next-generation tools for clinical-grade genomic analysis. This high-impact individual contributor (IC) role offers broad technical leadership scope, ideal for a scientist-engineer who thrives at the intersection of R&D innovation and clinical application, guiding projects from conception through validation under clinical laboratory standards. QUALIFICATIONS: • Education: • Master's or higher degree (PhD preferred) in Bioinformatics, Computational Biology, Genomics, Computer Science, Genomic Data Science, or related quantitative field. • Experience: • 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. • Experience working in a clinical genomics or regulated diagnostic environment strongly preferred. • 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. • Core Competencies: • 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. DUTIES AND RESPONSIBILITIES: • Scientific and Technical Leadership: • 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. • Algorithm, Model, and Pipeline Development: • 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. • Research and Development Innovation: • 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. • Computational Genomics, Data Science, and Statistical Modeling: • 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. • Cross Functional Collaboration: • 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. WHY JOIN BAYLOR GENETICS At Baylor Genetics, you'll join a world-class team dedicated to transforming clinical genomics through scientific excellence and technological innovation. As a Lead Bioinformatics Scientist, you'll have the opportunity to drive the development of new computational and analytical approaches that redefine how genomic data informs clinical decision-making. You will collaborate with leading scientists, engineers, and clinicians to deliver discoveries that matter, advancing the frontiers of precision medicine and improving lives through genomic insight. PHYSICAL DEMANDS AND WORK ENVIRONMENT: • Frequently required to sit • Frequently required to stand • Frequently required to utilize hand and finger dexterity • Frequently required to talk or hear • Frequently required to utilize visual acuity to operate equipment, read technical information, and/or use a keyboard • Occasionally exposed to bloodborne and airborne pathogens or infectious materials EEO Statement: Baylor Genetics is proud to be an equal opportunity employer dedicated to building an inclusive and diverse workforce. We do not discriminate based on race, religion, color, national origin, sex, sexual orientation, age, gender identity, veteran status, disability, genetic information, pregnancy, childbirth, or related medical conditions, or any other status protected under applicable federal, state, or local law. Equal Opportunity Employer This employer is required to notify all applicants of their rights pursuant to federal employment laws. 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