Scientific Technical Lead, Late Stage CMC
$109,500–$208,500 year
HybridNorth Chicago, Illinois, United States
Job Summary
Design, build, and deploy predictive and prescriptive models for 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. 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. Serve as a solution architect for AI and analytics initiatives within PDST, establishing modeling frameworks, validation protocols, and deployment standards that are scientifically rigorous, regulatory-aware, and built for long-term maintainability in a GxP environment. Define and drive data strategy for late-stage biologics programs, including data acquisition planning, ontology development, and integration across LIMS, MES, historian, and electronic batch record systems. Translate complex analytical outputs into clear, actionable scientific narratives for manufacturing, quality, regulatory, and executive audiences. Mentor junior scientists and analysts within PDST; contribute to a culture of technical excellence, intellectual curiosity, and continuous improvement.
Required Qualifications
- Bachelor's Degree in Computer Science or a related discipline
- 7 years' experience in IT and application program development
- Master's Degree
- 6 years' experience
- PhD
- 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
- 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
- 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
- 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
Desired Qualifications
- 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
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