Associate Director, Data Products and Reproducibility

Objective / Purpose

The Associate Director, Data Products and Reproducibility will be a scientific and technical leader within the Oncology Computational Biology Delivery team in the Computational Biology and Human Genetics organization. Reporting to the Director, Head of Oncology Computational Biology Delivery, this role will apply deep computational biology expertise and rigorous scientific judgment to ensure that oncology data and analyses are robust, reproducible, and trustworthy enough to support high-stakes research decisions across the Oncology Research pipeline.

The Associate Director will define and apply scientific standards for analysis, validation, data quality, reproducibility, and provenance, supported by sound engineering practices such as version control, testing, documentation, and monitoring. The successful candidate will combine deep scientific and computational biology expertise with fluency in modern data and AI practices, balancing responsive, program-specific scientific support with strategic investment in durable, well-validated resources that raise the rigor, consistency, and interpretability of oncology computational biology.

Accountabilities

• Establish and apply rigorous standards for analysis, validation, data quality, reproducibility, and provenance, ensuring that version sound practices exist in service of scientific rigor.
• Lead the design and stewardship of robust, reproducible analyses, workflows, and shared computational capabilities, applying sound scientific judgment to decide what merits durable investment versus one-off analytical support.
• Partner closely with other computational biologists and oncology research scientists to convert recurring scientific questions into well-validated resources and evidence-generation capabilities grounded in strong methodology.
• Collaborate with AI/ML, data science, software engineering, and data engineering teams to ensure analytical prototypes and AI concepts are scientifically sound before extension into reusable research capabilities.
• Set the scientific and technical direction, including roadmap, quality standards, and success measures, for oncology computational biology capabilities, in conjunction with the Director of Oncology Computational Biology Delivery and in alignment with Oncology Research and portfolio priorities.
• Develop and critically evaluate AI-enabled approaches for recurring research activities, and advance the team's scientific fluency in AI.
• Ensure analytical and AI-driven workflows and outputs are interpretable, well-documented, and scientifically defensible, so research teams understand not just what a result is, but why it can be trusted.
• Partner with biology, target validation, translational, pharmacology, and drug discovery teams to ensure analytical approaches are fit for purpose and defensible for program decisions.
• Manage priorities, risks, and resourcing tradeoffs across multiple programs; communicate scientific rationale, risks, and impact clearly to the Director and cross-functional leadership.
• Contribute to the broader oncology computational, data, and AI strategy, bringing scientific perspective to technology opportunities, standards, external collaborations, and investments.

Education & Competencies

• PhD in Computational Biology, Bioinformatics, Cancer Biology, Human Genetics, Genomics, Data Science, Computer Science, Engineering, or a related discipline, with 7 or more years of relevant post-degree experience and a demonstrated record of scientific impact (e.g., peer-reviewed publications, methods adopted by research teams, or field recognition).
• Deep expertise in computational biology data, bioinformatics workflows, multimodal biological data integration, and the scientific standards required for robust, reproducible computing.
• Demonstrated ability to apply sound scientific judgment when building computational, data, or AI capabilities — knowing what warrants durable investment versus lightweight support — and to set quality standards, roadmaps, and measurable outcomes accordingly.
• Strong grounding in the engineering practices that support scientific reproducibility (version control, testing, documentation, provenance, monitoring), applied with an understanding of why they matter, not just how to implement them.
• Experience designing or leading computational platforms or shared research capabilities that hold up to scientific scrutiny from the researchers who rely on them.
• Experience collaborating effectively with computational scientists, AI/ML scientists, data scientists, software engineers, and research-domain experts, with credibility among scientific stakeholders.
• Experience critically evaluating and applying AI-accelerated approaches to research workflows, with clear judgment on where AI adds rigor versus introduces risk.
• Ability to translate complex scientific and technical tradeoffs into clear, actionable communication for scientific and senior leadership audiences, and to manage priorities and resourcing risk in a complex, cross-functional research environment.