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Legal Data Analyst, Applied Data Science

Apple - Cupertino, United States of America

Posted Apr 2, 2026

Benefits

Parental leave
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Non-birth-parent leave
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Family-building benefits
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Mental health support
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Relocation assistance
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Childcare support
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Learning budget
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Verification
Not verified last checked Jun 13, 2026
Salary
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401(k) match
Listed Source: EMPLR_CONTRIB_INCOME_AMT. source Last checked Jun 13, 2026.

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Schedule

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Weekend work
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Application

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Deadline
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About this role

Legal Data Analyst, Applied Data Science Cupertino, United States of America Imagine what you could do here. At Apple, new ideas have a way of becoming great products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Are you passionate about turning complex legal data into actionable insights - and leveraging AI to do it faster and smarter? Do you thrive on uncovering patterns, trends, and anomalies that others miss - using both traditional analytics and AI-powered techniques? Can you bridge the gap between raw data, AI capabilities, and strategic decision-making? The Applied Data Science team within Legal Operations is building the data foundation that powers AI and analytics for a global legal organization. The Legal Data Analyst role is central to this mission - leveraging AI tools to accelerate analysis, preparing data for AI consumption, and surfacing insights that drive operational excellence. The Legal Data Analyst delivers analytical insights and ensures data readiness for AI and analytics across Legal Operations. You will leverage AI tools to accelerate your work, using AI for data profiling, anomaly detection, pattern recognition, and insight generation. You will analyze legal operations data to uncover trends, perform spend and matter analysis, build forecasting models, and prepare data for AI consumption. This role combines analytical rigor with AI fluency and deep understanding of legal operations to drive data-informed decisions. Leverage AI tools and techniques to accelerate data analysis, automate repetitive tasks, and surface insights

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