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Job Description
The Research Scientist I, Attribute Sciences Computational Biology, AI & Scientific Informatics will be responsible for accelerating adoption of artificial intelligence, agentic workflows, and advanced analytics across Analytical Development. This role will combine expertise in biological sciences, bioinformatics, data science, and generative AI to build AI-powered tools that improve scientific productivity, knowledge management, data accessibility, and decision-making to guide process development and product knowledge of viral vector-based gene delivery platforms and T cell therapies. In this role, the research scientist will partner closely with Process Development, Analytical Development, Manufacturing Sciences, Digital/IT, and Data Science teams to develop AI agents, retrieval-augmented generation (RAG) systems, workflow automations, and predictive models that leverage laboratory, process, and knowledge-management data. Ideal candidates would possess strong communications skills with both technical and non-technical stakeholders, curiosity about emerging AI capabilities, ability to understand scientific workflows and needs and translate those into working products, and software/programming skills sufficient to independently build and deploy AI and data science solutions.
Responsibilities include (but are not limited to):
AI Agent Development:
Design and deploy Copilot, Copilot Studio, or similar AI agents supporting scientific workflows.
Develop RAG-based knowledge assistants that integrate information from various enterprise knowledge sources.
Create agents that: answer scientific questions, retrieve and summarize technical information, generate draft reports, support investigations, assist with study planning and scheduling, support operational excellence.
Evaluate AI tool accuracy, groundedness, hallucination rates, performance and citation quality.
Bioinformatics & Scientific Computing
Analyze multi-modal biological datasets including NGS, omics, vector characterization, cell therapy process data.
Develop pipelines for automated data processing and visualization.
Knowledge Management and Automation
Support digital transformation initiatives involving electronic lab notebook and data lake infrastructure.
Automate repetitive scientific workflows and documentation tasks.
Build solutions that improve data discoverability and scientific knowledge reuse.
Cross-functional Collaboration
Work directly with scientists, IT and digital teams to identify opportunities where AI can improve productivity.
Translate scientific business problems into technical AI solutions.
Train stakeholders on effective use of AI tools and prompt engineering techniques.
Applied Data Science
Build predictive and descriptive models supporting process development and manufacturing.
Develop statistical tools for identifying relationships between process parameters and manufacturing outcomes.
Design dashboards and decision-support systems.
Basic Qualifications
Ph.D. in Bioinformatics, Computational Biology, Data Science, Computer Science or related discipline OR
Masters' Degree and 4+ years experience; OR
Bachelor's…