Scientific Technical Lead, Early Stage PDST CMC

Responsibilities: Modeling & Predictive Analytics  Develop, validate, and deploy predictive models that support process development decisions in early-stage biologics, including upstream bioprocess performance, downstream purification behavior, and critical quality attribute (CQA) outcomes.  Design and apply hybrid modeling approaches — combining first-principles process understanding with data-driven techniques — to maximize predictive power while maintaining scientific interpretability.  Build and evolve analytical frameworks that support digital twin concepts and in-silico process optimization, enabling smarter experimental strategies and accelerated development timelines.  Data Strategy & Architecture  Partner with process scientists, analytical scientists, and engineers to define data strategies for new programs — including what data to collect, how to structure it, and how to connect it across experimental campaigns.  Identify and address data quality, integration, and accessibility challenges that limit the value of existing datasets; advocate for and help implement improved data infrastructure within the pod.  Ensure that models, analyses, and data assets are built in a manner consistent with GxP principles and regulatory expectations, with appropriate documentation and traceability.  Intelligent Workflow Design  Function as a solution architect for analytical and AI-driven workflows: select the right approach for each problem — whether that means classical statistical methods, supervised or unsupervised machine learning, retrieval-augmented generation, multi-step orchestrated AI pipelines, or hybrid mechanisms — based on scientific need, data availability, and interpretability requirements.  Identify opportunities to automate, accelerate, or augment scientific workflows using intelligent tooling, and take ownership of scoping, building, and validating those solutions.  Stay current with the rapidly evolving landscape of AI and machine learning methods; evaluate emerging approaches for applicability to biologics development contexts.  Experimental Design & Decision Support  Collaborate with process development scientists to design experiments that are statistically rigorous, resource-efficient, and maximally informative — including design of experiments (DoE), Bayesian optimization, and active learning strategies.  Support process characterization studies by providing statistical analysis, model-based risk assessment, and data-driven identification of critical process parameters (CPPs) and their relationships to CQAs.  Contribute to technology transfer readiness by developing robust analytical frameworks that translate process understanding into transferable, defensible process knowledge.  Stakeholder Engagement & Scientific Communication  Translate complex quantitative analyses into clear, actionable insights for scientists, engineers, and senior leaders — bridging technical depth with business relevance.  Actively participate in cross-functional discussions, contributing data-driven perspective to program decisions, risk assessments, and development strategies.  Document, present, and defend analytical work in a manner appropriate for internal technical reviews and regulatory submissions.  Required: Bachelor’s degree in computer science or a related discipline with 7 years’ experience, Master's Degree with 6 years’ experience, or PhD with 2 years’ experience in IT, application program development 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  Experience with design of experiments (DoE) methodologies, Bayesian methods, or active learning in scientific applications.  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.  Preferred: Advanced degree (M.S. or Ph.D.) in Data Science, Biostatistics, Chemical or Biochemical Engineering, Computational Biology, or a closely related quantitative discipline preferred. 5+ years of hands-on experience building and deploying data science or machine learning solutions in a scientific or engineering-intensive environment.  Experience in working with&#