Senior Associate, Data Science & Applied AI

Job Summary

The Senior Associate, Data Science and Applied AI will develop AI methods and research applications at the intersection of materials science, biology and pharmaceutical drug containment and delivery.

Working with West’s R&D scientists, laboratory teams and AI engineers, this role will use machine learning, generative AI and scientific computing to accelerate materials characterization, experimental design, biological assessment and product development. The successful candidate will bring deep expertise in either materials science or biology, working knowledge of the other discipline, and demonstrated ability to apply AI to scientific problems.

Essential Duties and Responsibilities

• Identify high-value research opportunities where AI can accelerate materials discovery, biological assessment, formulation, testing or product development.
• Develop machine-learning and statistical models using experimental, materials, biological, imaging and scientific-literature data.
• Apply AI to areas such as polymer and coating performance, structure–property relationships, material–drug interactions, biocompatibility, extractables and leachables, and degradation or stability.
• Build predictive models, scientific knowledge systems and decision-support tools for West scientists and engineers.
• Apply techniques such as active learning, Bayesian optimization, graph-based learning, computer vision, multimodal modeling and scientific foundation models.
• Use generative AI and agentic workflows to support literature review, patent analysis, hypothesis generation and research synthesis.
• Partner with laboratory scientists to design experiments that validate model predictions and generate higher-value datasets.
• Integrate structured laboratory data with scientific documents, images and other unstructured information.
• Quantify model accuracy, uncertainty, generalizability and scientific limitations.
• Develop reproducible code, data pipelines, evaluation methods and technical documentation.
• Translate research prototypes into usable tools in partnership with AI engineering, D&T, Quality and R&D teams.
• Communicate findings, assumptions and recommendations to scientific, technical and business stakeholders.
• Support intellectual-property development through invention disclosures, technical reports and patent-related analysis.
• Follow West requirements for data integrity, quality, information security, responsible AI and scientific rigor.

Education

• Bachelors degree in Materials Science, Polymer Science, Biology, Bioengineering, Computational Biology, Chemistry, Chemical Engineering or a related scientific discipline required, Masters degree preferred.

Work Experience

• Minimum 3 years of relevant scientific research, applied AI or computational-modeling experience; doctoral research may be considered as relevant experience.
• Demonstrated experience applying AI, machine learning or advanced statistical methods to materials or biological research.
• Experience working with experimental scientists and translating scientific questions into computational approaches.

Preferred Knowledge, Skills and Abilities

• Deep expertise in either materials science or biology, with sufficient knowledge to work across the interface between materials and biological systems.
• Strong proficiency in Python and scientific-computing libraries.
• Experience with PyTorch, TensorFlow, scikit-learn or comparable machine-learning frameworks.
• Knowledge of experimental design, statistical inference, uncertainty quantification and model validation.
• Experience with materials informatics, bioinformatics, computational biology, chemoinformatics or molecular modeling.
• Familiarity with polymeric materials, elastomers, coatings, surface science, drug–container interactions or biocompatibility.
• Experience analyzing spectroscopy, microscopy, mechanical-testing, molecular, assay or other laboratory datasets.
• Familiarity with large language models, scientific retrieval-augmented generation and knowledge graphs.
• Exposure to Azure AI Foundry, Azure Machine Learning, Azure OpenAI or comparable cloud platforms.
• Ability to distinguish scientifically supportable conclusions from exploratory model outputs.
• Strong technical writing, communication and cross-functional collaboration skills.
• Experience in pharmaceuticals, medical devices, combination products or regulated research environments preferred.
• Candidates are encouraged to provide publications, patents, research projects, code repositories or other evidence of interdisciplinary scientific and AI work. #LI-Hybrid #LI-MC1

Travel Requirements

Physical Requirements

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