Business Lead, AI-native Target Identification & Validation (TIDVAL)

The Business Lead, AI-native Target Identification & Validation is central to realizing this vision. The scientist will be responsible for Takeda's next-generation target identification and validation AI-native software product: owning the scientific vision, customer experience, product strategy, roadmap, adoption, and ultimately the value it creates for our drug discovery. The Business Lead will define and answer what the target evaluation environment should be in a future where AI agents, foundational disease models, world models, predictive models, and human genetic, multi-omic, and clinical data at scale are the basis of how disease populations are stratified, genetic linkages to disease are established, and target hypotheses are built and defended. Our ambition is for this product to become the primary environment through which Takeda scientists will identify, evaluate, and select the targets behind the medicines of the future.

Purpose

The Business Lead, AI-native Target Identification & Validation is a foundational leadership role, acting as the owner of Takeda's Target Identification & Validation product family, an integrated family of capabilities spanning the target journey from initial biological hypothesis through validated, project-ready target. The role refines the strategic vision for future-state AI-native TIDVAL Labs of Tomorrow capabilities and drives its translation into AI-enabled research workflows, product direction, adoption, and measurable scientific and business value by working with both relevant internal and external stakeholders.

The successful candidate will lead this portfolio as a business owner on the side of the internal customer, combining deep target biology and target validation expertise with computational, data, and technology fluency to ensure that the products built are science-first, grounded in real pre-project research workflows, and capable of strengthening the target selection decisions that set portfolio direction. Your customers will be Takeda's disease area biologists, human geneticists, computational and systems biologists, bioinformaticians, translational scientists and therapeutic area leaders.

This role operates at the program leadership level, collaborating directly with Takeda's "Labs of Tomorrow" leadership team to shape portfolio priorities, resolve strategic tradeoffs, and ensure future-state target identification and validation capabilities advance from vision to measurable scientific and business impact. The role provides day-to-day direction to the technical leads and development squads working on the Target Identification & Validation product family, and is interacting closely with the SME leads. The role will also interface with the other Product Leads to ensure extensibility across the suites of products.

Accountabilities

• Own and refine the strategic vision for future-state TIDVAL capabilities, articulating the future-state target journey, redesigned for AI-native target identification and validation at data scale, and breaking it into the underlying capabilities delivered under a single mandate, in alignment with overall Research strategy.
• Deliver against that vision by providing business leadership, partnering with technical leads and development squads on scope, sequencing, and priority, and holding product teams accountable for outcomes, adoption, and measurable scientific and business impact.
• Define, track, and own KPIs, business value, adoption, and scientific impact across the product family, maintaining the integrated portfolio view that gives Research leadership a consolidated perspective on progress, dependencies, risks, and opportunities.
• Deeply understand the customer, the re-imagined AI-native workflows, the decisions they make, and unmet needs and represent them throughout development, working closely with the lead Subject Matter Experts to define target scientist experiences and to keep development anchored on target biology needs and on the priority capabilities that improve how target hypotheses are built, challenged, and selected.
• Set business priorities and direction across Research, Digital Data & Technology (DD&T), Computational Science, and external build partners, driving the cross-functional alignment needed to resolve risks, dependencies, and blockers across the product family.
• Work with the broader "Labs of Tomorrow" leadership team to set the digital product roadmap, scope, requirements, and investment priorities, balancing scientific value, strategic importance, and implementation readiness.
• Provide informed direction on the strategic technology choices underpinning the product family, including platform direction, architecture, and development sequencing, at the level of intent and trade-off rather than detailed evaluation or execution.
• Identify where AI agents, predictive models, and human genetic, multi-omic, and clinical data at scale can fundamentally redesign how disease populations are stratified and genetic linkages established, and translate those into opportunities in the products.
• Plan and drive adoption of delivered capabilities across therapeutic areas and platform functions, ensuring that they lead to measurable scientific and business value.

Qualifications & Competencies Expected

• Ph.D. in human genetics, computational biology, systems biology, molecular or cell biology or related field, with 8+ years of progressively responsible experience in pharmaceutical or biotech R&D.
• Demonstrated ownership of a product, platform, or capability portfolio end to end, including roadmap, delivery, and adoption.
• Demonstrated ability to set strategic direction, drive cross-functional alignment, and translate scientific ambition into executable product, workflow, adoption, and value-realization strategies in partnership with technology and product teams.
• Experience leading portfolio-level initiatives or transformation programs across discovery, computational science, digital, data, product, or technology teams.
• Hands-on coding experience, including experience coding earlier in career, with sufficient technical fluency to engage credibly with software engineers, computational scientists, data scientists, ML scientists, and technical leads.
• Demonstrated record of carrying an ambitious vision through to delivered and adopted capability.
• Strong business judgment, with ability to prioritize initiatives based on scientific value, strategic importance, feasibility, implementation readiness, and measurable impact.
• Strong experience in target identification and validation, ideally including one or more of human genetics and genomics, functional genomics, multi-omic data interpretation, disease population stratification, disease biology, target tractability assessment, translational biomarker strategy, and/or related AI/ML-enabled target discovery workflows.
• Strong fluency in computational, digital, data, and AI/ML concepts as applied to target identification and validation, sufficient to provide informed direction on technology choices and to test technical proposals on business grounds.
• Experience working across scientific, digital, data, informatics, automation, software, or product teams.
• Strong stakeholder management skills, including the ability to operate as a single point of accountability across multiple stakeholders and synthesize diverse inputs into clear priorities.
• Ability to influence without direct authority in a complex matrixed environment.
• Strong communication skills with the ability to engage senior leaders, scientific stakeholders, product managers, engineers, data scientists, and external partners.
• Experience directing external technology partners from the client side, holding them accountable to scope, timeline, and value while working within their product and delivery models.
• Preferred experience with scientific software or AI-driven discovery platforms, including target evidence and knowledge platforms, public and licensed genetic and omic data resources, knowledge graphs, literature mining tools, or biomarker data platforms.
• Preferred exposure to one or more of the following: AI/ML-enabled discovery applications, ontologies and controlled vocabularies for biological entities, analytical reproducibility and provenance practices, metadata standards, data models, or AI-ready scientific data.