Scientific Technical Lead, Late Stage CMC

What you'll do

• Build and deploy predictive models for process robustness, control strategy optimization, and commercial validation of late-stage biologics.
• Develop multivariate and time-series models to identify critical process parameters and predict manufacturing deviations.
• Create data infrastructure and analytical tools to facilitate seamless technology transfer from development to commercial manufacturing sites.
• Architect AI solutions using machine learning, statistical modeling, and hybrid mechanistic-empirical approaches in a GxP-regulated environment.
• Define data strategies including acquisition planning, ontology development, and integration across LIMS, MES, and historian systems.
• Translate complex analytical outputs into actionable scientific narratives for manufacturing, quality, and regulatory stakeholders.
• Establish modeling frameworks and validation protocols that comply with 21 CFR Part 11 and other regulatory standards.
• Mentor junior scientists and lead technical decision-making through evidence-based data science.

What we're looking for

• Bachelor's degree in Computer Science or related field with 7 years of experience; Master's with 6 years; or PhD with 2 years.
• Experience building and deploying data science or machine learning solutions in a scientific or engineering-intensive environment.
• Expert-level Python proficiency including the scientific ecosystem, modern data engineering, and cloud/big data tools.
• Mastery of business analytics tools such as R, Dataiku, AWS SageMaker, Spark, and Tableau.
• Strong foundation in statistical modeling, experimental design, multivariate analysis, and uncertainty quantification.
• Familiarity with knowledge graph, retrieval-augmented, or orchestrated AI/LLM-based systems for technical domains.
• Experience applying data science in a GxP-regulated environment with knowledge of FDA/EMA requirements.
• Familiarity with MLOps principles and model lifecycle management in regulated environments.
• Advanced degree in Data Science, Biostatistics, Engineering, or Computational Biology (preferred).
• 5+ years of experience building machine learning solutions in scientific or engineering environments (preferred).
• Experience in biologics manufacturing, late-stage process development, or commercial bioprocess operations (preferred).
• Experience with lab and manufacturing systems like LIMS, MES, DeltaV/historian, and eBR platforms (preferred).
• Track record of scientific communication through publications, regulatory submissions, or technical reports (preferred).
• Familiarity with technology transfer workflows and commercial process validation in a pharmaceutical context (preferred).