Associate Director, Tumor Functional Genomics and Perturbation Analytics

Takeda•Cambridge, MA

36d•$154,400 - $242,550•Hybrid

About The Position

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.

Requirements

PhD in Computational Biology, Bioinformatics, Cancer Biology, Functional Genomics, 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 tumor functional genomics and perturbation biology, including CRISPR screens, single-cell genomics, Perturb-seq, and related experimental and analytical systems.
Strong knowledge of cancer biology, tumor genomics, statistical genomics, multimodal data integration, bioinformatics, and quantitative analysis of complex biological datasets.
Demonstrated experience applying computational methods to functional-genomics and perturbation datasets in support of oncology drug discovery, target validation, biomarker discovery, or therapeutic development.
Experience developing reproducible, scalable analysis workflows, data resources, or computational products for scientific users.
Experience applying AI-accelerated ways of working in computational biology and evaluating the application of frontier AI methods or biological foundation models to cancer-omics and perturbation data.
Ability to translate complex functional-genomics results into clear biological hypotheses, evidence packages, and actionable recommendations for research and portfolio teams.
Understanding of modern software, data-product, and AI-product practices, including version control, testing, documentation, provenance, reproducible computing, deployment, monitoring, and lifecycle management.
Demonstrated ability to lead delivery across multiple programs and stakeholders, set priorities, manage risks, and communicate tradeoffs in a complex matrix environment.

Responsibilities

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 analytical prototypes and AI concepts into reliable, scalable, sustainable research capabilities.
Balance program-specific analytical support with investment in reusable functional-genomics capabilities and shared products.
Manage delivery priorities, risks, dependencies, and tradeoffs across multiple stakeholders; clearly communicate progress, scientific findings, limitations, and emerging opportunities to the Director and cross-functional teams.