Associate Engineering Fellow -Process & Data Architecture

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.

• 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…