Associate Engineering Fellow -Process Knowledge & Data Architecture

By clicking the “Apply” button, I understand that my employment application process with Takeda will commence and that the information I provide in my application will be processed in line with Takeda’s Privacy Notice and Terms of Use .  I further attest that all information I submit in my employment application is true to the best of my knowledge. Job Description OBJECTIVE:  Synthetic Molecule Process Development (SMPD) is responsible for the development of robust, sustainable and cost-effective processes for the manufacture of new synthetic molecule pharmaceuticals, along with methods for achieving and controlling high standards of purity and quality.  The Associate Engineering Fellow, Process Knowledge & Data Architecture will lead the design and implementation of a scalable, integrated data and knowledge architecture to support process development across SMPD. This role sits at the intersection of process science, data engineering, and digital strategy, enabling model-informed development and AI-driven workflows by transforming fragmented experimental and manufacturing data into structured, reusable knowledge assets. Working across matrix teams, the individual will define and implement SMPD’s data and knowledge strategy, enable data-driven decision-making, model development, and digital workflows, transforming fragmented datasets into a unified, high-value knowledge layer that accelerates development, improves process understanding, and supports lifecycle management. ACCOUNTABILITIES:  Own SMPD’s Knowledge Layer: Lead the design, governance, and lifecycle management of structured reaction, process, vessel, and material databases, establishing a unified and reusable process knowledge layer. Architect Connected Data Systems: Design, implement, and govern integrated data architectures linking chemistry, process parameters, scale, equipment attributes, and analytical results across laboratory and manufacturing environments (e.g., ELN, LIMS, PAT, MES). Enable Model-Informed Development: Build and maintain scalable data pipelines that transform laboratory and manufacturing data into reusable assets for mechanistic modeling, development of digital twins, and AI/ML applications. Drive FAIR & Data Standards: Define metadata models, ontologies, and data standards; enable ELN/PAT/TDP ingestion pipelines; and implement FAIR data principles to ensure data is findable, accessible, interoperable, and reusable. Ensure Governance & Compliance: Establish and enforce data governance frameworks, ensuring data quality, traceability, auditability, and lifecycle controls aligned with regulatory expectations (e.g., cGMP, data integrity). Drive Data Integration & Interoperability: Integrate disparate data sources and systems, eliminating silos and enabling a “single source of truth” across process development workflows. Partner Across Functions: Collaborate with Process Development, Modeling, Automation, and Engineering teams to ensure data is generated, captured, and structured to support advanced analytics, modeling, and decision-making. Bridge Science and Digital: Translate process development needs into data architecture and platform requirements, working closely with IT, data engineering, and digital teams to implement scalable, secure, and compliant solutions. Enable Data-Driven Decision Making: Develop data access, visualization, and reporting capabilities (e.g., dashboards) that support project teams and leadership in making informed decisions. Lead Technology Implementation: Support the selection, deployment, and integration of digital platforms (e.g., cloud environments, data lakes, APIs), ensuring scalability and alignment with enterprise architecture. Shape Strategy & Sustainability: Define SMPD’s connected data and model roadmap, identify and close architecture gaps, and partner with IT and Digital teams to ensure interoperability and long-term platform sustainability. Own Digital Twin Architecture & Governance: Define model standards, validation frameworks, version control, and lifecycle management for predictive and hybrid models deployed across development and manufacturing. Champion Data Culture & Capability Building: Promote best practices in data management, standards, and reuse; train and support scientists and engineers in adopting digital tools and workflows. Lead External Benchmarking & Best Practices: Stay informed on industry trends, emerging technologies, and regulatory expectations to continuously evolve SMPD’s data and knowledge capabilities. Operate in Matrix Teams: Influence and align cross-functional stakeholders to deliver integrated data solutions without direct authority. Operates as a technical leader within matrix teams, influencing stakeholders and driving alignment across functions without direct authority. Proactively identify vendors and builds relationships to gain access to technologies as needed to deliver against goals.  Manage key vendor relationships across multiple projects as appropriate, and proactively affects resolution of issues arising at vendors.     Represent Takeda and is an active member on pre-competitive collaborations with academic and industrial partners. Responsible for authoring relevant sections of regulatory documents, reports and peer reviewed manuscripts. EDUCATION, EXPERIENCE AND SKILLS:  Education and Experience: Required: PhD in Chemical Engineering, Chemistry, Data Science, or related field with 7+ years of relevant experience, or MS with 13+ years, or BS with 15+ years of relevant experience. Strong expertise in data architecture, data modeling, and data integration, preferably within pharmaceutical R&D or manufacturing environments. Experience working with scientific data systems (e.g., ELN, LIMS, MES) and integrating laboratory and manufacturing datasets. Proven experience designing scalable data platforms and pipelines, including cloud-based architectures (e.g., AWS, Azure) and modern data st