Director, Genomics & ctDNA Bioinformatics

AstraZeneca • Cambridge, MA

2d • $181,570 - $272,356

About The Position

AstraZeneca is seeking an experienced computational scientist within Biomarker Sciences & Technologies to lead bioinformatics strategy and analysis for minimally invasive and multimodal biomarkers, particularly ctDNA, genomic data, and oncology applications. The role provides hands-on scientific and technical leadership across biomarker research and clinical development, including development and application of 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 rigorous interpretation of biomarker results.

The Director will shape the use of computational genomics, ctDNA analytics, statistical modeling, and AI-enabled methods to address translational questions and support biomarker-driven decisions across the oncology portfolio. Responsibilities also include evaluating machine learning and AI approaches, building reusable analytical tools and data products, assessing model fitness and validity, and developing organizational capability through technical leadership and knowledge sharing.

Requirements

• At least 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.
• 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, including experience working within multidisciplinary teams.

Nice To Haves

• 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.

Responsibilities

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

Benefits

• Short-term incentive bonus opportunity
• Eligibility to participate in an equity-based long-term incentive program for salaried roles
• Retirement contribution for hourly roles
• Commission payment eligibility for sales roles
• Qualified retirement program (401(k) plan)
• Paid vacation and holidays
• Paid leaves
• Medical, prescription drug, dental, and vision coverage

What This Job Offers

• Job Type: Full-time
• Career Level: Director
• Education Level: Ph.D. or professional degree