Associate Scientific Technical Engineer II, PDS&T CMC

BioSpace; North Chicago (IL); On-site; USD 120,000 - 180,000; Full time

Job summary

AbbVie seeks a highly technical Data Engineer embedded in PDST to design, build, and operate production-grade data pipelines powering AI/ML, analytics, and automation across CMC and manufacturing. You will integrate MES, historians, LIMS, QMS, ERP, and instrument data into reliable data assets for scientists and engineers.

You will develop scalable data ingestion, harmonization, and governance capabilities, enabling end-to-end data lineage and enterprise data standards while collaborating with

Responsibilities

• Design scalable data ingestion pipelines connecting CMC and manufacturing source systems to centralized data environments.
• Develop harmonized data models and ontologies across sites and modalities.
• Implement data quality controls, lineage, and observability for production pipelines.
• Build governed data products (feature stores, curated datasets, vector-ready layers) for AI/ML use.
• Contribute to cloud-based platform design, CI/CD and infrastructure as code.

Job description

Company Description

About AbbVie

AbbVie's mission is to discover and deliver innovative medicines and solutions that solve serious health issues today and address the medical challenges of tomorrow. We strive to have a remarkable impact on people's lives across several key therapeutic areas including immunology, oncology and neuroscience - and products and services in our Allergan Aesthetics portfolio.

For more information about AbbVie, please visit us at www.abbvie.com. Follow @abbvie on LinkedIn, Facebook, Instagram, X and YouTube.

Job Description

While the AI innovation race in Biopharma is focused on Drug discovery, Product Development/ CMC represents the next barrier/ bottleneck. The complexity of biological systems, the rigor of regulatory expectations, the pace of pipeline growth, and the enormous value at stake make this one of the highest-leverage domains for applied data science and AI in the entire pharmaceutical value chain.

This position is a highly technical, AI-native role responsible for designing, building, and operating production-grade data pipelines and data products that power AI/ML, analytics, and automation across AbbVie's CMC and manufacturing ecosystem.

This role is embedded inside PDST and works at the frontier of pharmaceutical data engineering. You will integrate and harmonize data from the full spectrum of manufacturing and development systems — including MES (Manufacturing Execution Systems), historians, LIMS, QMS, ERP, and instrument platforms — and transform it into reliable, governed, semantically rich data assets that data scientists, process engineers, and AI systems can actually use.

Enterprise-scale scope: Enterprise-scale biologics portfolio spanning clinical, commercial, and lifecycle stages

Building AI playbook for the future: First-in-AbbVie and first-in-biologics analytical approaches; you build the AI playbook for the future

Growth and Impact: Direct impact on regulatory submissions, commercial readiness, and manufacturing decisions through deep cross-functional exposure to manufacturing, quality, regulatory, and scientific leadership

Mission: Every model you build helps ensure safe, reliable medicines reach patients at scale

Responsibilities

Data Ingestion & Integration

• Design and implement scalable, robust data ingestion pipelines that connect CMC and manufacturing source systems — including MES (Manufacturing Execution Systems), process historians, LIMS, QMS, ERP platforms, and instrument data sources — to centralized and federated data environments.
• Build connectors, adapters, and integration layers that handle the heterogeneous data formats, protocols, and latency profiles characteristic of pharmaceutical manufacturing environments.
• Support both batch and real-time/streaming data patterns, selecting appropriate architectures based on use case requirements.

Data Harmonization & Semantic Modeling

• Develop and maintain harmonized data models and ontologies that bring consistency to CMC and manufacturing data across sites, systems, and modalities.
• Execute semantic mapping efforts that align source system fields, units, and identifiers to enterprise data standards and scientific meaning.
• Collaborate with process scientists, analytical chemists, and manufacturing engineers to ensure data models accurately reflect domain reality.

Data Quality, Observability & Governance

• Implement automated data quality controls, validation frameworks, and anomaly detection mechanisms across pipeline layers.
• Build and maintain data lineage documentation and metadata infrastructure, enabling full traceability from source system to AI model input.
• Establish pipeline observability practices — monitoring, alerting, SLA tracking — to ensure data product reliability in production.
• Support data governance practices aligned with GxP requirements, 21 CFR Part 11, and AbbVie data standards.

AI/ML Enablement & Data Product Development

• Architect and deliver governed, versioned, reusable data products purpose-built for AI/ML consumption, including feature stores, curated datasets, and vector-ready data layers for RAG and LLM applications.
• Partner closely with data scientists, ML engineers, and process modelers to understand model data requirements and translate them into reliable, scalable data infrastructure.
• Accelerate AI program delivery by eliminating data bottlenecks — not by workarounds, but by solving root causes structurally.

Platform & Operational Enablement

• Contribute to the design and evolution of PDST's cloud-based data platform, including lakehouse architecture, data cataloging, access control, and compute infrastructure.
• Write and maintain infrastructure-as-code, CI/CD pipelines, and automated testing frameworks for data systems.
• Support platform onboarding of new CMC data domains and manufacturing sites, ensuring consistent application of standards and patterns.
• Provide operational support for production data pipelines, maintaining uptime and data freshness commitments.

Stakeholder Engagement &amp