Business Lead, AI-native Large Molecule / Biologics Discovery

3316 Takeda Development Center Americas•Boston, MA

1d•Hybrid

About The Position

Takeda is transforming medicine discovery by building a new Research engine that embeds AI, advanced data platforms, and laboratory automation into how scientists design and discover molecules, run experiments, and make decisions. The Labs of Tomorrow vision connects computational science, scientific data, and automated experimental workflows into a closed-loop discovery engine to enable smarter studies, shorten learning cycles, and deliver clearer evidence earlier in discovery, ultimately aiming to discover differentiated medicines for patients. The Business Lead, AI-native Large Molecule Discovery is central to this vision, responsible for Takeda’s next-generation large molecule discovery AI-native software product. This role involves owning the scientific vision, customer experience, product strategy, roadmap, adoption, and the value created for drug discovery. The Business Lead will define what the large molecule discovery platform should be in a future where AI agents, predictive models, scientific data, and automated experimentation are the basis of large molecule drug discovery. The ambition is for this product to become the primary environment for Takeda scientists to design and discover future large molecule medicines.

Requirements

Ph.D. in computational biology, protein engineering, structural biology, bioengineering, biologics discovery or related field, with 10+ 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 discovery-stage large-molecule drug discovery, ideally including one or more of computational biology, protein engineering, antibody discovery, sequence and construct design, expression and purification, characterization, developability assessment, and/or related AI/ML-enabled discovery workflows.
Strong fluency in computational, digital, data, automation, and AI/ML concepts as applied to large-molecule drug discovery, 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.

Nice To Haves

Preferred experience with scientific software or AI-driven discovery platforms, including ELN/LIMS, sequence and construct registration, reagent and lineage tracking, high-throughput expression, automated purification, or analytical data platforms.
Preferred exposure to one or more of the following: AI/ML-enabled discovery applications, protein and antibody design tools, developability prediction, metadata standards, data models, or AI-ready scientific data.

Responsibilities

Own and refine the strategic vision for future-state Large Molecule capabilities, articulating the future-state biologics discovery journey, redesigned for AI-native, and increasingly automated drug discovery, 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 discovery science needs and on the priority capabilities that improve how biologics discovery work gets done.
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, data, and automation can fundamentally redesign scientific workflows and translate those into opportunities in the products.
Plan and drive adoption of delivered capabilities across the biologics portfolio, ensuring that they lead to measurable scientific and business value.