Note: The job is a remote job and is open to candidates in USA. Baylor Genetics is seeking a highly experienced and innovative Senior Scientist in NGS Algorithm Development to lead the design, optimization, and implementation of computational algorithms for next-generation sequencing (NGS) data. This role focuses on detecting and interpreting genomic features and chromosomal abnormalities while ensuring analytical accuracy and scalability across products.

Responsibilities

• Lead the design, optimization, and implementation of scalable NGS algorithms and pipelines for detection and interpretation of complex genomic features, including SNVs/Indels, CNVs, STRs, methylation, trisomy, PGx variants, and variants in homologous and homopolymer regions
• Lead the design and optimization of targeted NGS panels for existing and new products
• Drive end-to-end development, validation, benchmarking, and integration of NGS algorithms and analysis pipelines using internal and public truth sets
• Collaborate closely with assay scientists, bioinformatics teams, software engineers, and other partners to translate biological and product requirements into computational solutions
• Provide technical and project leadership to ensure analytical accuracy, robustness, scalability, and continuous improvement across products
• Support technology transfer, pipeline updates, and production deployment, and contribute to scientific publications, conference presentations, and intellectual property development

Skills

• Ph.D. in Bioinformatics, Computational Biology, Genomics, or a related discipline
• Minimum 5 years of experience in NGS algorithm development
• Proficiency in Python, R, C++, and workflow orchestration tools
• Deep understanding of: Read alignment and variant calling (e.g., BWA-MEM, minimap2, GATK, DeepVariant) for germline or/and somatic variants
• Deep understanding of: CNV modeling, STR detection tools and methylation callers
• Deep understanding of: Homologous region analysis and control gene normalization
• Deep understanding of: PGx variant interpretation and allele resolution
• Experience with long-read technologies (ONT, PacBio) and signal-level data
• Strong analytical, problem-solving, and communication skills
• Somatic variant calling by short-reads or/and long-reads sequencing
• Experience with machine learning models for variant classification
• Knowledge of clinical genomics and regulatory standards
• Familiarity with pharmacogenomic databases (e.g., PharmGKB, CPIC)

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