Sr Data Scientist/AI Applications Engineer

• Frame and deliver AI-enabled use cases: translate ambiguous business, scientific, engineering, and operational needs into actionable product, data, and AI use cases, then design and implement practical workflow solutions that solve real Process Development problems. 

• Build workflow, data, and knowledge foundations: develop agents, automation patterns, retrieval flows, context engineering approaches, and human-in-the-loop workflows, while working with structured and unstructured experimental data, metadata, protocols, reports, controlled vocabularies, taxonomies, semantic models, entity relationships, graph data models, entity resolution approaches, and semantic query patterns. 

• Support product ownership and adoption: work with product owners and business partners to shape backlog, prioritization, release planning, stakeholder expectations, feedback loops, adoption approach, and measurable client outcomes for AI-enabled capabilities. 

• Provide technical leadership in delivery: help data scientists, AI engineers, ontology specialists, product owners, business partners, and other stakeholders work through delivery decisions, dependencies, and tradeoffs without adding people-management scope to the role. 

• Develop evaluation, evidence, and reusable solutions: create benchmarks, test sets, acceptance criteria, evaluation harnesses, quality checks, and evidence packs, and package prompts, retrieval logic, data contracts, notebooks, scripts, or services so solutions can be reused rather than remaining one-off prototypes. 

• Document and communicate the technical basis: explain assumptions, data lineage, model or agent behavior, limitations, validation evidence, and appropriate use to technical and non-technical stakeholders. 

Basic Qualifications

• Bachelor's OR Master's degree and minimum of 7 years of related experience in engineering, computer science, data science, applied mathematics, statistics, life sciences, or a related technical field  

Required Skills:

• Hands-on experience building AI, machine learning, generative AI, agentic, retrieval-augmented generation, workflow automation, or decision-support systems. 

• Strong programming skills in Python and experience with modern software engineering practices, including version control, testing, packaging, documentation, and reproducible workflows. 

• Experience with experimental data, scientific metadata, laboratory or process development workflows, knowledge management, data pipelines, or data product development. 

• Experience designing evaluation methods for AI or analytics systems, including benchmarks, test sets, model or agent performance metrics, error analysis, and acceptance criteria. 

• Ability to translate ambiguous scientific or operational problems into clear technical requirements, implementation plans, and measurable outcomes. 

• Understanding of biopharmaceutical development, process development, manufacturing science, analytical development, or related regulated technical environments. 

• Experience with cloud, data platform, API, notebook, or application development patterns used to deploy reusable analytical or AI capabilities to internal users. 

• Strong written and verbal communication skills, including the ability to document technical assumptions, limitations, data lineage, validation evidence, and user guidance. 

• Demonstrated ability to independently uncover, structure, and resolve issues associated with scientific, engineering, data, or AI-enabled delivery projects. 

• Ability to work effectively in a matrixed global environment across AIN and U.S. time zones, with scientists, engineers, product owners, data strategists, and software/platform partners.