Live opening · Posted 7 hours ago
Manager - Design Intelligence & AI Catalyst (Hybrid)
AbbVie · South San Francisco, CA, United States
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At a glance
The key details from the original listing.
CompanyAbbVie
LocationSouth San Francisco, CA, United States
Job typeFull-time
Work modeHybrid
Source
Smartrecruiters
ListedPosted 7 hours ago
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About the role
Description supplied by the original job listing.
The Manager, Design Intelligence & AI Catalyst will build, deploy, and maintain M&S and AI solutions that automate and augment capabilities within Data & Statistical Sciences (DSS) to support development decision-making. This role sits within the Data and AI Acceleration (DAIA) team and supports the broader DSS organization. Develop solutions for prioritized use cases and build tools that close adoption gaps and meet end-user needs. Identify the right tool for the right problem — varying from R packages, dashboards, to agentic AI frameworks.
Responsibilities
Develop and maintain solutions for prioritized M&S and AI use cases, from R packages and dashboards to agentic AI frameworks
Build tools and solutions that address adoption gaps, ensuring delivered capabilities meet end-user needs
Conduct upfront feasibility assessments and architect solution design — documenting design rationale for when to use deterministic approaches (e.g., R packages), interactive dashboards, or LLM-powered agentic workflows
Create and maintain comprehensive benchmark test sets to ensure output quality, accuracy, and reproducibility
Maintain and iterate on deployed tools based on user feedback, bug reports, and evolving requirements
Translate requirements into production-ready solutions through iterative collaboration with stakeholders
Minimum Qualifications
Master’s degree with 5-7 years of experience OR PhD in Statistics, Data Sciences, Computer Science, or a related quantitative discipline with 2+
Strong hands-on development experience in R
Experience building interactive dashboards and data applications
Experience with agentic AI development (tool-use agents, orchestration frameworks, retrieval-augmented generation)
Good statistical knowledge to ensure analytical rigor in delivered solutions
Familiarity with clinical development processes and trial design concepts
Software engineering best practices (version control, testing, CI/CD)
Ability to evaluate trade-offs between solution approaches and calibrate complexity to the problem at hand
Good communication skills with ability to collaborate iteratively with stakeholders
Employment type
Full-time
Work arrangement
Hybrid
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