Objective / Purpose :
This leadership role is pivotal in driving target identification and validation, biomarker discovery, and translation into the clinic, spanning neuroscience, oncology, gastroenterology and inflammation therapeutic areas. This role is responsible for leading a global team that leverages cutting-edge computational biology and AI / ML techniques to rigorously identify and evaluate disease-target-biomarker relationships and utilize human genetics to derive novel insights for drug discovery, indication expansion, and safety assessments.
Accountabilities :
1. Leadership and Team Management
- Establish the Computational Biology & Human Genetics strategy and goals
- Lead a dynamic team of ~30 computational biologists, statistical geneticists, and data scientists to accelerate Takeda’s drug discovery and development pipeline
- Provide scientific leadership to ensure that Takeda remains at the forefront of science and methodology in computational biology and genetics
- Foster a culture of high-performance and growth, through mentoring and career development
- Drive deep cross-functional collaboration with other computational science and R&D teams (e.g., discovery biology, screening and validation, pre-clinical sciences, translational medicine and biomarker teams, computational chemistry, quantitative sciences, therapeutic area units and engineering)
2. Target Identification & Validation
Lead efforts from a computational systems biology and statistical genetics perspective to discover and validate targets across disease areas, with an emphasis on integrating genetic, multi-omic, and functional genomic dataLead -omic data strategies to further establish disease knowledge base across TAs via internal cross-functional and external collaborationsWork closely with target screening and validation teams to i) rapidly iterate between experimentation and analysis, and ii) systematically translate preclinical models to human disease endpointsApply cutting-edge AI / ML methods to identify novel therapeutic targets by analyzing large-scale genomic and patient-derived dataOversee the application of human genetic evidence to validate disease mechanisms and prioritize targets for drug development3. Biomarker Discovery & Clinical Translation
Drive the discovery and refinement of biomarker-disease-target relationships, ensuring that findings are actionable and translatable to the clinicWork closely with translational and clinical teams to ensure that computational insights inform patient selection, indication expansion, biomarker identification, and development of surrogate endpoints of clinical efficacy4. AI / ML & Cutting-Edge Computational Approaches
Lead the integration of AI / ML techniques and multi-modal data analysis in the discovery process to uncover novel insights into disease mechanisms, biomarker identification, and target refinement, as well as systematically evaluate and prioritize targets-indications.Collaborate with informatics / IT and other computational science teams to enhance computational infrastructure, ensuring scalability and effectiveness of data processing and modeling effortsApply multi-omics integration (e.g., genomics, proteomics, transcriptomics), multi-modal and systems biology approaches to provide holistic insights into disease biology5. Human Genetics & Data Integration
Utilize human genetics data to guide the identification of novel therapeutic targets, inform indication expansion, and assess the impact of genetic variations on drug safety and efficacyOversee the integration of genetic association studies (e.g., GWAS) with clinical datasets to identify potential targets and biomarkers for personalized medicineWork with clinical and preclinical teams to evaluate genetic risk factors and their implications for target selection and patient segmentation in clinical trials6. Strategic Oversight & Cross-Functional Collaboration
Partner with senior leadership to define and execute strategic goals for the computational biology and human genetics teams, ensuring alignment with broader company objectivesCollaborate with other departments (e.g., preclinical & translational sciences, therapeutic area units, global advanced platform, and drug discovery units) to ensure that insights from computational biology and genetics are effectively integrated into drug discovery programs7. External Collaborations & Innovation
Identify and foster partnerships with academic institutions, biotechnology companies, and technology providers to leverage impactful data sets and cutting-edge scientific and technological developmentsLead the evaluation and integration of emerging technologies and methodologies to enhance team capabilitiesEducation & Competencies (Technical and Behavioral) :
PhD in Computational Biology, Bioinformatics, Human Genetics, Genomics, or related fields.15 years of experience in the pharmaceutical or biotechnology industry, with a strong track record in computational biology, human genetics, or genomic medicineExperience in leading interdisciplinary teams in a large-scale research setting, with strong management and mentoring skillsExpertise in AI / ML applications in drug discovery and biomarker developmentIn-depth knowledge of transcriptomic (bulk, single cell, spatial) and proteomic approaches and state-of-the-art analytic methodologies including multi-omic / modal data integrationIn-depth knowledge of genetic data analysis, including GWAS, whole-genome sequencing, genetic risk modeling, and multi-omics integrationStrong background in target identification, biomarker discovery, and clinical translation, with a focus on therapeutic developmentDemonstrated ability to collaborate cross-functionally, with experience in working with clinical teams and translating computational findings into actionable clinical insightsExcellent communication skills, with the ability to present complex scientific concepts to non-scientific stakeholders and senior leadershipJ-18808-Ljbffr
