Associate Director, Tumor Functional Genomics and Perturbation Analytics

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, Tumor Functional Genomics and Perturbation Analytics 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.

The Associate Director, Tumor Functional Genomics and Perturbation Analytics will be a key scientific and delivery 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 lead the development and application of tumor functional-genomics and perturbation analytics capabilities to advance the Oncology Research pipeline.

The role will provide deep expertise in computational biology, tumor functional genomics, perturbation biology, CRISPR screening, single-cell genomics, Perturb-seq, and related systems. The Associate Director will develop analyses, data resources, evidence packages, and reusable computational products that generate novel biological insight and support target identification and validation, biomarker discovery, patient stratification, and therapeutic development.

The successful candidate will combine recognized scientific expertise with strong delivery leadership, rigorous analytical judgment, and a product-oriented approach. The individual will collaborate with oncology research scientists, target validation teams, AI/ML scientists, data scientists, software engineers, and data engineers to define priorities and deliver scalable, reproducible computational capabilities. The role will also advance AI-enabled ways of working and frontier approaches for extracting biological insights from cancer-omics and perturbation data.

• Lead the delivery of tumor functional-genomics and perturbation-analytics analyses, data resources, evidence packages, and reusable computational products supporting Oncology Research and portfolio decisions.

• Provide scientific leadership in tumor biology, functional genomics, perturbation biology, cancer genomics, and computational oncology.

• Develop and apply robust analytical approaches for CRISPR screens, pooled and arrayed perturbation experiments, single-cell genomics, Perturb-seq, and related functional-genomics systems.

• Integrate perturbation, tumor-omics, single-cell, genetic, phenotypic, and translational datasets to generate insights supporting target identification and validation, mechanism-of-action hypotheses, biomarker identification, patient stratification, and therapeutic development.

• Partner with biology, target validation, translational, pharmacology, and drug discovery teams to define scientific questions, design analytical strategies, and ensure that computational findings are interpretable, actionable, and aligned with program needs.

• Establish appropriate standards for scientific validation, data quality, reproducibility, version control, testing, documentation, provenance, usability, maintenance, and ownership for perturbation-analytics workflows and data products.

• Develop reusable computational methods, workflows, and analysis frameworks that improve the consistency, speed, accessibility, and scientific quality of functional-genomics support across the Oncology portfolio.

• Define and implement AI-enabled approaches for design and analysis of functional genomics and perturbation biology experiments, including methods for data discovery, experimental evidence synthesis, analysis planning, workflow execution, quality control, interpretation, and generation of scientific deliverables.

• Evaluate and apply frontier AI biological foundation models and related approaches leveraging cancer-omics and perturbation data to generate novel insights for oncology target identification, therapeutic development, and patient selection.

• Collaborate with AI/ML, data science, software engineering, and data engineering teams to develop…