Legal Data Analyst, Applied Data Science

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.\\n\\nAre you passionate about turning complex legal data into actionable insights — and leveraging AI to do it faster and smarter?\\nDo you thrive on uncovering patterns, trends, and anomalies that others miss — using both traditional analytics and AI-powered techniques?\\nCan you bridge the gap between raw data, AI capabilities, and strategic decision-making?\\n\\nThe 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 at scale\\nUse AI-assisted data profiling, anomaly detection, and pattern recognition to identify data quality issues and trends faster\\nAnalyze legal operations data to surface trends, patterns, and actionable insights across matters, spend, contracts, and vendors\\nDevelop spend analysis, matter forecasting, and resource utilization models that inform strategic decisions — using AI to enhance predictive capabilities\\nProfile and assess data quality across legal systems including matter management, contract management, eBilling, and document management\\nCleanse, standardize, and enrich data to meet quality thresholds required for AI and analytics consumption\\nValidate entity data (law firms, timekeepers, vendors, counterparties) to support entity resolution initiatives\\nBuild analytical models that support rate negotiations, vendor performance evaluation, and outside counsel management\\nPartner with AI/ML Engineers to identify opportunities where AI can automate or augment analytical workflows\\nDocument data lineage, business rules, and transformation logic\\nPartner with Data Stewards across practice groups to enforce data quality at the source\\nDevelop and maintain analytical dashboards, data quality reports, and monitoring tools\\nTranslate analytical findings into clear recommendations for practice group and leadership audiences

4+ years of experience in data analysis, business intelligence, and analytics roles\\nStrong proficiency in SQL, Python, and experience with analytical and data profiling tools\\nExperience using AI and LLM-based tools to accelerate analytical work\\nExperience with statistical analysis, trend analysis, and forecasting techniques\\nExperience working with enterprise data systems (ERP, CRM, matter management, or similar)\\nStrong analytical and problem-solving skills with attention to detail\\nAbility to communicate analytical findings and data quality insights to technical and non-technical stakeholders\\nExperience building in and presenting with BI and visualization tools (Tableau, Power BI, or similar)\\nDeriving and defining KPIs and other business impact metrics for leadership

Experience with legal operations data (matter management, eBilling, CLM, document management)\\nDemonstrated ability to integrate AI tools into daily analytical workflows — not just experimentation, but production use\\nUnderstanding of prompt engineering techniques to get better results from AI tools\\nUnderstanding of legal spend management, LEDES/UTBMS billing codes, and outside counsel metrics\\nExperience with predictive analytics and forecasting models\\nFamiliarity with data governance frameworks and data stewardship models\\nExperience with data quality tools (Great Expectations, Monte Carlo, or similar)\\nKnowledge of entity resolution concepts and master data management\\nUnderstanding of how data quality impacts AI/ML model performance\\nExperience in corporate legal department or professional services environment