Associate Director, Data Products and Reproducibility

Are you looking for a patient-focused, innovation-driven company that will inspire you and empower you to shine? Join us as an Associate Director, Data Products and Reproducibility in our Cambridge office. At Takeda, we are transforming the pharmaceutical industry through our R&D-driven market leadership and being a values-led company. To do this, we empower our people to realize their potential through life-changing work.

Certified as a Global Top Employer, we offer stimulating careers, encourage innovation, and strive for excellence in everything we do. We foster an inclusive, collaborative workplace, in which our global teams are united by an unwavering commitment to deliver Better Health and a Brighter Future to people around the world. Here, you will be a vital contributor to our inspiring, bold mission.

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.

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.

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,…