Job Description
The SpringCube team curated the following job opportunity to help you in your job search. Explore the position below to find your next career move.
Company Overview
A global technology leader is seeking a Legal Data Analyst to join its Applied Data Science team within Legal Operations. The organization is building the data foundation that powers AI and analytics across a global legal function, enabling smarter decision-making, operational excellence, and scalable AI adoption. This role offers the opportunity to work at the intersection of legal operations, data analytics, and artificial intelligence, helping transform complex legal data into actionable business insights.
The organization is looking for a data-driven professional who is passionate about leveraging AI and analytics to uncover trends, patterns, and opportunities within legal operations data. The ideal candidate will combine strong analytical capabilities with AI fluency and a deep understanding of data quality, governance, and business intelligence practices.
Key Responsibilities
- Leverage AI tools and techniques to accelerate data analysis, automate repetitive tasks, and generate insights at scale.
- Utilize AI-assisted data profiling, anomaly detection, and pattern recognition to identify trends and data quality issues.
- Analyze legal operations data to uncover actionable insights across matters, spend, contracts, vendors, and related functions.
- Develop spend analysis, matter forecasting, and resource utilization models to support strategic decision-making.
- Enhance predictive capabilities through the use of AI-powered analytical techniques.
- Profile and assess data quality across legal systems, including matter management, contract management, eBilling, and document management platforms.
- Cleanse, standardize, and enrich datasets to meet quality requirements for AI and analytics consumption.
- Validate entity data, including law firms, timekeepers, vendors, and counterparties, to support entity resolution initiatives.
- Build analytical models that support vendor performance evaluation, rate negotiations, and outside counsel management.
- Partner with AI/ML engineers to identify opportunities where AI can automate or augment analytical workflows.
- Document data lineage, business rules, and transformation logic.
- Collaborate with data stewards and stakeholders to enforce data quality standards at the source.
- Develop and maintain dashboards, monitoring tools, and data quality reporting solutions.
- Translate analytical findings into clear recommendations for leadership and business stakeholders.
Required Qualifications
- 4+ years of experience in data analysis, business intelligence, or analytics-related roles.
- Strong proficiency in SQL, Python, and analytical or data profiling tools.
- Experience using AI and large language model (LLM) tools to accelerate analytical workflows.
- Experience with statistical analysis, trend analysis, and forecasting methodologies.
- Experience working with enterprise data systems such as ERP, CRM, matter management, or similar platforms.
- Strong analytical, problem-solving, and critical-thinking skills with exceptional attention to detail.
- Ability to communicate analytical findings to both technical and non-technical audiences.
- Experience developing reports and dashboards using business intelligence tools such as Tableau, Power BI, or similar platforms.
- Experience defining and tracking KPIs and business impact metrics for leadership reporting.
Preferred Qualifications
- Experience working with legal operations data, including matter management, eBilling, contract lifecycle management (CLM), and document management systems.
- Demonstrated success integrating AI tools into production analytical workflows.
- Understanding of prompt engineering techniques for improving AI-generated outputs.
- Knowledge of legal spend management, LEDES/UTBMS billing codes, and outside counsel performance metrics.
- Experience building predictive analytics and forecasting models.
- Familiarity with data governance frameworks and data stewardship practices.
- Experience with data quality platforms such as Great Expectations, Monte Carlo, or similar tools.
- Understanding of entity resolution concepts and master data management.
- Knowledge of how data quality impacts AI and machine learning model performance.
- Experience working within a corporate legal department or professional services environment.
Disclaimer
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