Director, Computational Biology

Takeda•Boston, MA

20h•Hybrid

About The Position

The Director, Oncology Computational Biology will be a key scientific leader within the Computational Biology and Human Genetics (CBHG) team. This individual will serve as a hands-on expert in cancer genetics/genomics and computational approaches to oncology, driving the discovery and prioritization of novel oncology targets and therapeutic concepts across Takeda’s Oncology portfolio. The successful candidate will combine deep expertise in cancer biology and human genetics with advanced computational approaches, including statistical genomics, machine learning, and multimodal data integration, to generate rigorous, biologically grounded insights from complex high-dimensional datasets. The candidate will be fluent in, and bring a vision for, methods for accelerating target discovery through AI. Working closely with oncology researchers, translational scientists, AI/ML experts, and data scientists, the Director will help shape Takeda’s oncology pipeline through innovative and scientifically robust target identification and validation strategies.

Requirements

PhD in Computational Biology or a related discipline, plus 10+ track record of scientific innovation and impact
Recognized expert in cancer genetics/genomics and oncology computational biology
Expertise in machine learning and complex algorithms required
Industry experience supporting oncology target ID
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

Nice To Haves

Experience in AI/LLMs/biological foundation models strongly preferred

Responsibilities

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 the application of 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 ID/validation efforts