Knowledge & Innovation Analyst - Litigation

Pillsbury Winthrop Shaw Pittman•Nashville, DC

About The Position

The Data Analyst will support the Data Science & AI Engineering team by developing analytics, reporting, and dashboard solutions that provide practice leadership, executive leadership, and business stakeholders with meaningful, real-time insights into firm operations. Areas of focus include profitability, staffing, pricing, pipeline activity, and the impact of AI-driven initiatives. The role works with cross-functional stakeholders to deliver data-driven analyses and actionable insights that support strategic decision-making, operational effectiveness, and continuous business improvement across the Firm.

Requirements

Bachelor’s degree in Statistics, Mathematics, Economics, Computer Science, Data Science, or a related qualitative field, or equivalent experience in lieu of a degree.
3 to 7 years of analytics, business intelligence, or data science experience; ideally including experience in a professional services firm, financial services firm, or other complex, regulated environment.
Strong proficiency in SQL and experience with modern business intelligence and data visualization platforms such as Power BI, Tableau, Looker, or ThoughtSpot.
Demonstrated ability to design and structure effective data models by transforming complex or unstructured data into scalable, efficient, meaningful reporting solutions.
Working knowledge of Python or R for advanced analysis, automation, and data manipulation beyond standard BI and SQL capabilities.
Excellent written, verbal, and visual communication skills, including the ability to present complex analytical findings clearly and effectively to executive and business audiences.
Strong analytical and critical-thinking skills, including the ability to validate data accuracy, identify inconsistencies, and apply sound judgment to reporting and analysis.
Ability to sit and stand for extended periods.
Ability to lift up to 15 pounds.

Nice To Haves

Experience with modern data platforms and technologies, including dbt, Snowflake, BigQuery, Databricks, or similar environments.
Familiarity with legal industry systems and data sources, including matter management, time and billing systems (e.g., Aderant or 3E), document management systems, intake and conflicts platforms, and financial or pipeline reporting data.
Understanding of version control practices, analytics testing frameworks, CI processes, and data observability methodologies.
Exposure to AI and machine learning concepts, including the ability to support analytics and reporting related to AI utilization, adoption, and operational impact.

Responsibilities

Partner with practice leaders and firm leadership to identify business needs and translate them into scalable analytics solutions and reporting tools.
Develop and maintain dashboards and reporting solutions that provide insight into matter performance, financial results, operational trends, and AI utilization metrics.
Perform ad hoc analyses and deliver clear, actionable insights for strategic and operational decision-making.
Establish, maintain, and oversee key firm metrics, including data definitions, calculation methodologies, governance standards, and reporting integrity.
Collaborate with engineering teams to support and optimize data models, integrations, transformations, and the warehouse/lakehouse layer that power reporting and analytics solutions.
Monitor and promote data quality and integrity by identifying inconsistencies, reporting gaps, and system issues, and partnering with appropriate teams on corrective solutions.
Develop scalable, reusable analytics assets, including standardized data models, documented metrics, and extensible dashboards.
Collaborate with Data Scientists and MLOps / AI Engineering teams to integrate AI utilization and impact metrics into firmwide analytics and reporting, including ML and LLM outputs in business-facing tools.
Partner with Knowledge Management and Innovation teams to ensure business unit data is accurately captured, structured, and leveraged for dashboards and operational insights.
Present analytical findings and recommendations clearly and actionably to support business and strategic decision-making.