Scientific Technical Lead, Late Stage CMC

AbbVie•North Chicago, IL

21h•$109,500 - $208,500

About The Position

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. We here at BTS - PDST, are building a dedicated, AI-native team that is driving cutting edge programs across early stage, late stage and commercial product development to accelerate E2E product development and launch, maximize yields of block buster products. Through our deep collaboration with PDST scientists we are boldly reimagining how AbbVie can bring our pipeline products and lifesaving drugs to patients faster, safer and in cost effective manner fueled by AI. Late-Stage Biologics Data Scientist is a senior individual contributor role built for a scientist-engineer who thinks in systems, builds with purpose, and leads through technical credibility. This role is a shaper of outcomes. You will embed AI and advanced analytics directly into AbbVie's late-stage biologics pipeline — including process characterization studies, technology transfer to commercial manufacturing sites, process robustness and commercial lifecycle optimization. You will architect data solutions, build and deploy predictive models, and establish the analytical foundation that enables AbbVie to make faster, smarter, more defensible decisions at every stage of commercial biologics development. 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

Requirements

Bachelor's Degree in Computer Science or a related discipline with 7 years’ experience in IT and application program development; or Master's Degree with 6 years’ experience; or PhD with 2 years’ experience.
Respective years of hands-on experience building and deploying data science or machine learning solutions in a scientific or engineering-intensive environment.
Expert-level Python proficiency; deep familiarity with the scientific Python ecosystem (NumPy, pandas, scikit-learn, PyTorch or TensorFlow, modern data engineering (cloud, big data, pipeline orchestration)
Strong foundation in business analytics, with mastery of tools such as R, Dataiku, AWS SageMaker, Spark, Tableau
Strong foundation in data science methods, statistical modeling, experimental design, multivariate analysis, and uncertainty quantification — with the ability to choose, justify, and communicate methodological choices rigorously
Familiarity with knowledge graph, retrieval-augmented, or orchestrated AI/LLM-based systems applied to scientific or technical domains
Experience applying data science in a GxP-regulated environment, with working knowledge of FDA/EMA expectations for process validation, continued process verification (CPV), and control strategy.
Familiarity with MLOps principles, model lifecycle management, or deployment of analytical tools in regulated or enterprise environments.
Ownership orientation: you define your own problem space, drive solutions to completion, and hold yourself accountable to outcomes — not just outputs.
Solution-architect instinct: you think before you build, consider the full landscape of available approaches, and choose tools based on fit-for-purpose reasoning rather than familiarity or trend.
Scientific integrity: you build models you can explain, defend, and improve — and you apply the same standard to the work of others.
Influence through credibility: you earn the confidence of scientists, engineers, and quality professionals by being right, being clear, and being useful — not by title or volume.
Bias for impact: you are drawn to problems where the stakes are high and the analytical opportunity is real, and you are energized rather than intimidated by ambiguity.

Nice To Haves

Advanced degree (M.S. or Ph.D.) in Data Science, Biostatistics, Chemical or Biochemical Engineering, Computational Biology, or a closely related quantitative discipline.
5+ years of hands-on experience building and deploying data science or machine learning solutions in a scientific or engineering-intensive environment.
Direct experience in biologics manufacturing, late-stage process development, or commercial bioprocess operations — including familiarity with upstream (cell culture, fermentation) and/or downstream (purification, formulation) unit operations.
Exposure to stability program analytics, comparability assessments, or post-approval change management from a data and modeling perspective.
Experience in working with data from diverse lab and manufacturing systems (LIMS, MES, DeltaV/historian, eBR platforms) and building scalable data pipelines for process analytics.
Track record of scientific communication — publications, regulatory submissions, technical reports, or equivalent — that demonstrates the ability to convey complex analytical work clearly and credibly.
Familiarity with technology transfer workflows, process characterization study design, or commercial process validation (PPQ/PV) in a biologics or pharmaceutical context.

Responsibilities

Design, build, and deploy predictive and prescriptive models that support process robustness assessment, control strategy optimization, and commercial process validation across late-stage biologics programs.
Develop multivariate and time-series modeling approaches to identify critical process parameter interactions, predict process drift, and support proactive deviation prevention at commercial manufacturing sites.
Apply advanced statistical and machine learning methods — including dimensionality reduction, anomaly detection, Bayesian inference, and hybrid mechanistic-empirical models — to characterize complex bioprocess behavior and establish meaningful process design spaces.
Build and maintain golden batch frameworks and optimization models that serve as living benchmarks for process performance across sites and over time.
Lead the development of data infrastructure and analytical tools that enable intelligent, data-driven technology transfer from development to commercial manufacturing — reducing transfer risk and compressing timelines.
Build cross-site process intelligence systems that allow PDST and manufacturing teams to compare, contextualize, and act on process data across geographically distributed sites and diverse equipment trains.
Partner with manufacturing science and quality teams to define data requirements, establish data standards, and ensure analytical continuity from process development through commercial operations.
Serve as a solution architect for AI and analytics initiatives within PDST — evaluating problems holistically and selecting the right combination of approaches, whether that means classical statistical models, modern machine learning, retrieval-augmented knowledge systems, orchestrated analytical agents, or purpose-built hybrid mechanisms.
Establish modeling frameworks, validation protocols, and deployment standards that are scientifically rigorous, regulatory-aware, and built for long-term maintainability in a GxP environment.
Contribute to PDST's AI roadmap by identifying high-value opportunities, scoping solutions, and advocating for the infrastructure investments needed to sustain analytical excellence.
Define and drive data strategy for late-stage biologics programs — including data acquisition planning, ontology development, quality standards, and integration across LIMS, MES, historian, and electronic batch record systems.
Champion data literacy and modeling best practices across PDST and its manufacturing and quality stakeholder community.
Ensure that models, analyses, and data assets are documented, version-controlled, and maintained to standards consistent with regulatory expectations including 21 CFR Part 11, ICH Q8/Q9/Q10, and relevant FDA/EMA guidance.
Translate complex analytical outputs into clear, actionable scientific narratives for manufacturing, quality, regulatory, and executive audiences.
Influence technical decision-making without formal authority — earning trust through scientific rigor, transparent methodology, and demonstrated business impact.
Mentor junior scientists and analysts within PDST; contribute to a culture of technical excellence, intellectual curiosity, and continuous improvement.