Position Summary
We are seeking a highly experienced and innovative Senior NGS Algorithm Developer to lead the design and optimization of computational pipelines for next-generation sequencing (NGS) data. This role focuses on the detection and interpretation of a wide range of genomic features, including small variants (SNVs/Indels), copy number variants (CNVs), short tandem repeats (STRs), methylation patterns, and variants in homologous and homopolymer regions.The ideal candidate will hold a Ph.D. in Bioinformatics, Computational Biology, Genomics, or a related field, and have at least 5 years of hands-on experience in algorithm development for NGS applications.
Experience in pharmacogenomics (PGx) variant calling, including complex loci such as CYP2D6, is strongly preferred.
Key Responsibilities
- Design and implement robust, scalable algorithms for: small variants, CNV detection, STR genotyping, methylation analysis, variant resolution in homologous and homopolymer regions, PGx variant calling, including hybrid alleles and copy number estimation in complex loci (e.g., CYP2D6, TPMT, UGT1A1)
- Integrate phasing and allele-specific analysis for small variants and methylation
- Collaborate with assay scientists and software engineers to translate biological requirements into computational solutions
- Benchmark algorithm performance using public and internal truth sets
- Maintain reproducible workflows using tools like Nextflow, and Docker
- Contribute to publications, presentations, and intellectual property development
Required Qualifications
- 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)
- CNV modeling, STR detection tools and methylation callers
- Homologous region analysis and control gene normalization
- PGx variant interpretation and allele resolution
- Experience with long-read technologies (ONT, PacBio) and signal-level data
- Strong analytical, problem-solving, and communication skills
Preferred Qualifications
- Experience with machine learning models for variant classification
- Knowledge of clinical genomics and regulatory standards
- Familiarity with pharmacogenomic databases (e.g., PharmGKB, CPIC)
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.
