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The AI Product Analyst is responsible for translating business needs into AI-enabled product capabilities by combining strong product analysis, data interpretation, and hands-on use of modern AI tools.
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This role partners closely with product, engineering, data science, and business stakeholders to define requirements, generate insights, and support delivery of intelligent, data-driven features.
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A key expectation of this role is the ability to work closely with business teams in real-world contexts to deeply understand problems, rapidly iterate on solutions, and translate evolving needs into scalable
product capabilities.
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The individual must be comfortable operating in ambiguous environments, engaging directly with end users, and refining requirements continuously based on feedback and observed usage.
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This includes rapidly prototyping ideas, leveraging AI tools to validate concepts, and bridging the gap between business intent and technical implementation.
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The role also involves leveraging generative AI and enterprise AI platforms to accelerate requirement drafting, scenario analysis, and decision-making.
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Responsibilities include developing user stories, defining acceptance criteria, validating AI outputs, supporting testing of AI-driven features, and ensuring alignment with responsible AI practices and enterprise
standards.
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Strong experience in product or business analysis with ability to translate requirements into user stories and acceptance criteria
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Ability to work directly with business stakeholders to understand problems, iterate quickly, and refine solutions based on feedback
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Comfortable operating in ambiguous, fast-evolving environments with minimal upfront definition
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Hands-on experience with AI tools such as ChatGPT, Claude, Gemini, Microsoft Copilot, and GitHub Copilot
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Proficiency in prompt engineering and AI-assisted workflows (requirements, analysis, documentation)Strong analytical and problem-solving skills with a data-driven mindset
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Experience working in Agile/SAFe environments with backlog management and sprint execution
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Ability to validate AI outputs for accuracy, completeness, and business relevance
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Strong collaboration skills across product, engineering, and data science teams
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Understanding of APIs, data flows, and system interactions
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Knowledge of Responsible AI principles
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Experience working in high-touch, iterative delivery environments with close business engagement
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Experience on AI/ML-enabled products or platforms
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Familiarity with LLM-based systems, AI agents, or AI platforms
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Experience using AI for: Requirement generation, Scenario modeling, Test case creation, Rapid prototyping of workflows
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Basic knowledge of data analysis tools, SQL, or data modeling
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Exposure to healthcare, insurance, or regulated domains
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Experience with tools such as Jira, Confluence, or similar