Associate Engineering Fellow -Process Knowledge & Data Architecture
$154,400–$242,550 year
On-siteCambridge, Massachusetts, United States
Job Summary
Lead the design and implementation of a scalable, integrated data and knowledge architecture to support synthetic molecule process development. Own SMPD's Knowledge Layer by governing reaction, process, and material databases, while architecting connected data systems that link chemistry, scale, and analytical results across laboratory and manufacturing environments. Build scalable data pipelines to enable model-informed development, digital twins, and AI/ML applications by transforming fragmented datasets into reusable assets. Define metadata models, ontologies, and FAIR data standards to ensure interoperability and regulatory compliance, then integrate disparate sources to eliminate silos. Collaborate with cross-functional teams to translate process needs into platform requirements, develop visualization capabilities for data-driven decision-making, and champion data culture through training and vendor management.
Required Qualifications
- PhD in Chemical Engineering, Chemistry, Data Science, or related field
- 7+ years of relevant experience
- MS with 13+ years of relevant experience
- BS with 15+ years of relevant experience
- Strong expertise in data architecture, data modeling, and data integration
- Experience working with scientific data systems (e.g., ELN, LIMS, MES)
- Experience integrating laboratory and manufacturing datasets
- Proven experience designing scalable data platforms and pipelines
- Experience with cloud-based architectures (e.g., AWS, Azure)
- Experience with modern data stacks
- Strong understanding of data governance, metadata management, and FAIR data principles
- Experience enabling analytics, modeling, or AI/ML applications through structured data frameworks
- Understanding of pharmaceutical process development and/or manufacturing workflows
- Understanding of CMC and cGMP considerations
- Demonstrated ability to work in cross-functional, matrix environments
- Ability to influence stakeholders across scientific and technical domains
- Strong communication skills
- Ability to translate complex technical concepts into actionable insights
- Technical writing skills
- Analytical and Problem Solving Skills
- Teamwork
- Communication Skills
- Organization
- Technical subject matter expertise
- Knowledge Sharing
- Resource Management
- External Involvement
- Leadership Skills
- May require approximately 10% travel
Desired Qualifications
- Experience with ontologies, knowledge graphs, or semantic data models
- Experience supporting digital twins, advanced process control, or hybrid modeling
- Track record of driving enterprise data initiatives or platform deployments
- Experience with regulatory data integrity requirements (e.g., ALCOA+)
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