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We’re seeking an AI Business Analyst Intern to help identify, define, and assess AI and GenAI opportunities that improve customer experiences, operational efficiency, and decision-making. You’ll work with business stakeholders and technical teams to translate problems into clear requirements, success metrics, and delivery plans—supporting proof-of-concepts and scaling high-value AI solutions.
Job Responsibility:
Partner with stakeholders to understand business goals, pain points, and current processes
document as-is and to-be workflows
Identify and frame AI/GenAI use cases
develop problem statements, hypotheses, and value narratives
Translate needs into business requirements, user stories, and acceptance criteria (e.g., for RAG copilots, automation, forecasting, classification)
Define KPIs and measurement plans (accuracy/quality, time saved, adoption, cost, risk) and support experimentation/evaluation
Perform data discovery: identify source systems, data gaps, data quality issues, and requirements for governance/privacy
Build business cases including ROI, cost/benefit, risk assessment, and implementation options
Support solution delivery by coordinating with product, data science, engineering, and security teams
track progress and dependencies
Identify, implement and test software solutions that could best address the problem
Create clear stakeholder communications: decks, one-pagers, process maps, and demo narratives
Prepare summaries, presentations and documentation of the implemented solution
Requirements:
Ability to work full-time for 12 weeks during Summer 2026
Currently pursuing a BS/MS in Business Analytics, Information Systems, Economics, Engineering, Data Science, or related field
Strong analytical and problem-solving skills
comfortable working with ambiguity
Ability to write clear requirements and communicate with both business and technical teams
Basic understanding of data concepts (tables, joins, metrics definitions) and the analytics lifecycle
Understanding AI/ML concepts (training vs inference, evaluation, overfitting, metrics) and algorithms