Director, Integrative Sciences

Role Summary

Director, Integrative Sciences leading the development and execution of biomarker analysis plans and overseeing data analytics strategies to support early-stage research programs, disease area strategy and clinical trial readiness within Oncology Translational Medicine.

Responsibilities

• Provide leadership and strategic direction for the analysis of complex biological data generated in early research programs and clinical trials, ensuring robust interpretation and reporting to inform key decisions, clinical study reports, and regulatory submissions.
• Lead the development and implementation of translational & biomarker analysis plans in collaboration with biomarker, biology and translational leads, ensuring alignment with Oncology Translational Medicine strategies.
• Drive the integration and analysis of large-scale, high-dimensional, and multimodal biomarker datasets from internal and external sources to enhance understanding of mechanisms of action, resistance, patient selection, new indications, and biologically driven combination strategies.
• Act as a key leader in the matrix by collaborating with biomarker, biology, translational research and AI/ML teams to evaluate and apply complementary data analytics approaches, ensuring the delivery of meaningful insights.
• Provide high-level accountability for clear and timely communication of data analysis outputs, complex analytical principles, and models to diverse stakeholders, including senior leaders and non-analytical colleagues.
• Champion data integrity principles aligned with human data quality standards and FAIR principles across the matrix team.
• Serve as a strategic partner in integrating academic biomarker partnership data and technology evaluation data generated by the Oncology Research Unit.
• Lead internal and external biomarker data analysis efforts (e.g., real-world data) to support pipeline growth, including life cycle management plans, biomarker prevalence in patient populations, and leveraging data analytics outputs to enable strategic decision-making with a focus on the earlier stage portfolio.
• Provide leadership and accountability for advanced analytics and target/pathway analysis to support evaluation of due diligence business development asset evaluations.
• Drive the seamless transition of early-stage research programs into clinical trial readiness by leveraging biomarker data insights and translational medicine strategies.

Qualifications

• PhD degree or equivalent experience/training in computational biology, bioinformatics, machine learning, or a related field.
• 7+ years of applied experience in Pharma/Biotech or an academic setting, with a focus on oncology research, biomarker analysis, and translational medicine.
• Demonstrated ability to lead and influence cross-functional teams in a matrix environment, driving alignment and delivering impactful outcomes.
• Proficiency in coding skills (e.g., R, Python) and strong working knowledge of common bioinformatics databases, resources, and tools.
• Extensive experience with next-generation sequencing data and oncology research programs, including preclinical and early translational studies.
• Proven ability to communicate analytical principles, complex data insights, and results to senior leaders, multidisciplinary teams, and non-analytical stakeholders.
• Experience with Good Clinical Practice (GCP) principles and working on clinical studies or programs transitioning into clinical trials.
• Demonstrated ability to create impactful data visualization outputs and foster collaboration across multi-disciplinary teams.
• Experience with GitHub, development of R Shiny applications/R markdown, and working in cloud or high-performance computing environments.

Preferred Qualifications

• Proven leadership experience in biomarker discovery and validation in early drug development programs.
• Expertise in analyzing complex high-dimensional datasets (e.g., single-cell and spatial transcriptomics, proteomics, cfDNA) using state-of-the-art models and analytical approaches.
• Strong knowledge of data and metadata best practices (e.g., FAIR principles, data standards, cloud environment analytical tools).
• Advanced knowledge of statistical and analytical methods relevant to the analysis of complex high-dimensional heterogeneous datasets.
• Strong track record of integrating preclinical and clinical biomarker data to inform translational medicine strategies.
• Familiarity with regulatory requirements and data standards for transitioning programs into clinical trials.
• Strategic mindset with the ability to influence and drive decision-making in a matrix environment.