Director, Genomics & ctDNA Bioinformatics

What You'll Do

Lead Genomics and ctDNA Bioinformatics

• Lead bioinformatics strategy and hands-on analysis for genomic and ctDNA biomarker studies across discovery, translational research, and clinical development.
• Develop, evaluate, and apply reproducible workflows for sequencing quality control, variant detection, copy-number and other genomic analyses, ctDNA quantification, molecular response, and longitudinal disease monitoring.
• Design rigorous benchmarking studies to compare assays, computational pipelines, calling thresholds, and analytical approaches using reference materials, orthogonal methods, tissue–plasma comparisons, and clinical datasets.
• Build statistical, predictive, and mechanistic models that integrate genomic, ctDNA, clinical, and other biomarker data to generate actionable biological insights and support development decisions.
• Define fit-for-purpose analysis plans, performance metrics, validation criteria, and standards for reproducibility, traceability, statistical rigor, and scientific interpretation.
• Communicate analytical findings, assumptions, limitations, and recommendations clearly to multidisciplinary project teams and senior scientific stakeholders.

Drive AI Enablement and Innovation

• Evaluate and implement machine learning and AI methods for genomic interpretation, ctDNA signal detection, multimodal data integration, and biomarker modeling.
• Identify opportunities where advanced computational methods can improve analytical sensitivity, robustness, scalability, or biological insight.
• Develop and promote scalable, reusable analytical tools, pipelines, and data products that enable consistent application across studies and programs.
• Assess model performance, generalizability, bias, interpretability, and fitness for intended use, and ensure appropriate scientific and statistical validation.
• Build organizational capability through technical leadership, code and methods review, education, and knowledge sharing.

Minimum Qualifications

• 7+ years of experience in bioinformatics, computational biology, genomics, biomarker analysis, or a related field.
• PhD in Bioinformatics, Computational Biology, Genomics, Statistics, Computer Science, Biomedical Engineering, or a related quantitative field.
• Deep experience analyzing next-generation sequencing data and developing or applying bioinformatics workflows in biomedical research or drug development.
• Demonstrated expertise in genomics, biomarker analysis, statistical modeling, data integration, and rigorous interpretation of complex molecular and clinical datasets.
• Experience designing benchmarking or validation studies and evaluating analytical performance, including sensitivity, specificity, precision, reproducibility, and sources of bias.
• Proficiency in Python and/or R and experience developing reproducible, version-controlled analytical workflows in a collaborative computing environment.
• Ability to communicate complex technical concepts, analytical limitations, and decision-relevant conclusions to diverse scientific audiences.
• Strong scientific leadership and collaboration skills, with experience working within multidisciplinary teams.

Desirable Qualifications

• Direct experience with liquid biopsy and ctDNA analysis, including low-frequency variant detection, tumor fraction estimation, molecular response or progression, minimal residual disease, or longitudinal monitoring.
• Experience benchmarking genomic assays or bioinformatics pipelines across platforms, vendors, sample types, or orthogonal technologies.
• Knowledge of somatic variant calling, copy-number analysis, error suppression, sequencing quality metrics, assay limit-of-detection assessment, and tissue–plasma concordance analyses.
• Experience developing predictive, longitudinal, or multimodal models that combine genomic biomarkers with clinical, imaging, or other molecular data.
• Experience applying machine learning or AI to biomedical data, with attention to model validation, interpretability, generalizability, and bias.
• Knowledge of oncology biomarker development, translational science, precision medicine, and clinical development.