Director, Computational Biology

Job Title

Director, Oncology Computational Biology

The Director, Oncology Computational Biology will serve as a key scientific leader within the Computational Biology and Human Genetics (CBHG) team. This role drives the discovery and prioritization of novel oncology targets and therapeutic concepts across Takeda’s Oncology portfolio, combining deep expertise in cancer biology and human genetics with advanced computational approaches, including statistical genomics, machine learning and multimodal data integration.

• Serve as a scientific leader in cancer genetics/genomics and computational oncology, driving AI/ML‑enabled target identification, prioritization and validation strategies to advance a differentiated oncology discovery portfolio.
• Lead advanced computational approaches to oncology target discovery and validation, integrating multimodal datasets to uncover novel biological mechanisms, therapeutic opportunities, biomarkers and patient stratification hypotheses.
• Develop and apply rigorous computational frameworks that integrate genomic, transcriptomic, functional dependency, clinical, proteomic and other high‑dimensional datasets to generate actionable insights supporting oncology target identification and validation.
• Establish scalable AI/ML‑driven ways of working for oncology target discovery, enabling systematic hypothesis generation, target evaluation and evidence integration across diverse internal and external data sources.
• Partner closely with oncology biology, target validation, translational and drug discovery teams to ensure computational insights are biologically grounded, experimentally testable and directly aligned with target validation and portfolio progression activities.
• Collaborate with AI/ML, data science and data engineering teams to build scalable analytical capabilities, reusable workflows and high‑quality oncology data assets that accelerate target discovery and validation across the Oncology Research organization.
• Help shape Takeda’s oncology computational and data strategy, identifying opportunities to enhance target discovery and validation capabilities through external collaborations, strategic partnerships, emerging AI technologies and novel data resources.
• Influence scientific and portfolio decisions through clear communication of complex computational, biological and translational findings to cross‑functional teams and senior leadership.
• Lead and contribute to complex, multidisciplinary oncology discovery programs in a highly collaborative matrix environment, serving as a key computational driver of oncology target identification and validation efforts.

• PhD in Computational Biology or a related discipline, plus 10+ years track record of scientific innovation and impact.
• Recognized expert in cancer genetics/genomics and oncology computational biology.
• Expertise in machine learning and complex algorithms; experience in AI/LLMs/biological foundation models strongly preferred.
• Industry experience supporting oncology target identification.
• Demonstrated ability to lead complex projects in a matrix environment.
• Strong organizational skills; ability to set priorities and meet program objectives and timelines.
• Strong written and oral communication skills to diverse audiences.

The position will be based in Cambridge, MA and is classified as hybrid under Takeda’s Hybrid and Remote Work policy.

Takeda is proud in its commitment to creating a diverse workforce and providing equal employment opportunities to all employees and applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity, gender expression, parental status, national origin, age, disability, citizenship status, genetic information or characteristics, marital status, status as a Vietnam era veteran, special disabled veteran, or other protected veteran in accordance with applicable federal, state and local laws, and any other characteristic protected by law.