Director, Genomics & ctDNA Bioinformatics

AstraZeneca is committed to using genomics, data science, and artificial intelligence to accelerate scientific discovery and drug development. Within Biomarker Sciences & Technologies, we are seeking an experienced computational scientist to lead bioinformatics strategy and analysis for minimally invasive and multimodal biomarkers, with particular emphasis on circulating tumor DNA (ctDNA), genomic data, and oncology applications. This role will provide hands-on scientific and technical leadership across biomarker research and clinical development, developing and applying robust methods for genomic and ctDNA analysis, assay and pipeline benchmarking, data integration, and predictive and mechanistic modeling. The successful candidate will translate complex molecular and clinical datasets into actionable evidence, establish fit-for-purpose analytical standards, and guide teams in the rigorous interpretation of biomarker results. This position offers a unique opportunity to shape how computational genomics, ctDNA analytics, statistical modeling, and AI-enabled methods are used to answer complex translational questions and support biomarker-driven decisions across the oncology portfolio. 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. Why Join Us? This role offers the opportunity to help shape the future application of AI and advanced analytics within biomarker science. You will work alongside leading experts across translational medicine, data science, and technology, contributing to innovative approaches that advance scientific understanding and support the development of life-changing medicines. The annual base pay for this position ranges from $181,570.40 -$272,355.60 USD Annual. Hourly and salaried non-exempt employees will also be paid overtime pay when working qualifying overtime hours. Base pay offered m