Associate Director, Tumor Genomics and Target Credentialing

Takeda•Cambridge, MA

36d•$154,400 - $242,550•Hybrid

About The Position

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 Genomics and Targeted Credentialing 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. Objective / Purpose The Associate Director, Tumor Genomics and Target Credentialing 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-genomics capabilities, analyses, evidence packages, and reusable computational products that advance Oncology Research pipeline decisions. The Associate Director will provide scientific leadership in cancer genetics, tumor genomics, statistical genomics, and multimodal data integration to support target identification and validation, biomarker identification, patient stratification, and therapeutic development. The role will partner closely with oncology research scientists, target validation teams, translational scientists, AI/ML scientists, data scientists, and data engineers to define high-value scientific questions and deliver interpretable, actionable evidence. The successful candidate will combine deep expertise in cancer genomics with strong scientific judgment and delivery leadership. This individual will contribute to AI-enabled ways of working by developing, evaluating, and adopting reusable approaches for data discovery, evidence synthesis, target assessment, analysis planning, quality control, result interpretation, and generation of scientific deliverables.

Requirements

PhD in Computational Biology, Bioinformatics, Cancer Biology, Human Genetics, Genomics, Data Science, or a related discipline, with 7 or more years of relevant post-degree experience and a demonstrated record of scientific impact; or equivalent combination of education and experience.
Deep expertise in cancer genetics, tumor genomics, statistical genomics, bioinformatics, and quantitative analysis of complex biological datasets.
Demonstrated experience integrating multimodal data to support oncology target identification, target validation, biomarker development, patient stratification, or therapeutic development.
Experience developing and communicating rigorous target-credentialing or evidence-synthesis packages that influence scientific and portfolio decisions.
Strong scientific judgment and demonstrated ability to translate complex computational, biological, and translational findings into clear, actionable recommendations for cross-functional teams.
Experience applying AI-accelerated approaches in computational biology, including the evaluation and responsible use of frontier AI methods and biological foundation models leveraging cancer-omics data.
Experience delivering complex computational, data, or AI capabilities across multiple programs, stakeholders, and competing priorities in a robust and reusable manner.
Understanding of modern data-product and AI-product practices, including version control, testing, documentation, provenance, reproducible computing, deployment, monitoring, and lifecycle management.
Demonstrated ability to set priorities, manage delivery risks, communicate tradeoffs, and influence scientific decisions in a complex matrix environment.

Responsibilities

Lead the delivery of tumor-genomics analyses, data resources, target-credentialing evidence packages, and reusable computational capabilities that support Oncology Research and portfolio decisions.
Provide scientific leadership in cancer genetics, tumor genomics, statistical genomics, bioinformatics, and quantitative analysis of complex biological datasets.
Integrate complex oncology data types—including tumor-omics, human genetics, clinical, translational, and functional evidence—to generate insights supporting target identification and validation, biomarker identification, patient stratification, and therapeutic development.
Establish and apply rigorous scientific standards for target credentialing, including evidence evaluation, analytical validation, data quality, reproducibility, provenance, documentation, and transparent interpretation of findings.
Partner with biology, target validation, translational, pharmacology, and drug discovery teams to define scientific questions, prioritize analyses, and ensure that deliverables are actionable and aligned with program needs.
Develop reusable, scalable approaches for target assessment and evidence synthesis that improve the speed, consistency, accessibility, and scientific quality of computational biology support across the Oncology portfolio.
Define and execute AI-enabled approaches within the tumor genomics and target credentialing domain, prioritizing opportunities based on scientific value, user needs, feasibility, and organizational readiness.
Guide the development and application of reusable AI-enabled skills for activities such as data discovery, literature and evidence synthesis, target assessment, analysis planning, workflow execution, quality control, result interpretation, and scientific communication.
Collaborate with AI/ML, data science, software engineering, and data engineering teams to translate analytical prototypes and AI concepts into reliable, scalable, and sustainable research capabilities.
Ensure appropriate standards for version control, testing, documentation, reproducible computing, data and analytical provenance, usability, maintenance, and ownership of the team's data products and analytical workflows.
Manage competing program priorities, delivery risks, dependencies, and tradeoffs; communicate progress, risks, emerging opportunities, and scientific impact clearly to the Director and cross-functional stakeholders.