Top Senior Analysis Reporting Jobs in Washington, DC

The role involves designing and implementing data science solutions, leveraging AI and machine learning, preparing data, creating visualizations, leading internal data initiatives, and collaborating with various teams to deliver impactful insights and drive decision-making.

The Senior Vice President will advise clients on research strategies, lead teams, drive revenue growth, and oversee research programs while fostering collaboration and professional growth.

Lead the migration and implementation of the Ex Libris Alma/Primo VE platform, ensuring high-quality metadata and data cleanup. Collaborate with teams to validate and refine configurations, standards, and workflows while providing training on new practices.

The Geospatial Developer will create geospatial datasets, perform data analysis, and develop maps and visualizations for federal clients. Responsibilities include data preparation, ensuring data accuracy, collaborating with teams, and supporting decision-making processes with geospatial insights.

The role involves developing and implementing pricing strategies in a law firm environment, focusing on alternative fee arrangements and enhancing profitability through data analysis and collaboration with attorneys and stakeholders.

The Senior Data Analyst will analyze data, build dashboards and models, automate processes, and collaborate with various teams to derive actionable insights.

As a Marketing Data Strategist, you will develop data strategies, measure marketing performance, and collaborate with analytics and IT teams to ensure data quality and governance.

The Analytic Analyst at Amentum will conduct data analysis, prepare reports, and support organizations by providing threat analysis. Responsibilities include coordinating meetings, quality control, collaboration with experts, and developing analytical strategies.

The Principal Analytics Architect will design and implement analytical models to enhance decision-making at CVS Health, focusing on translating operational data into structured frameworks, supporting measurement and business insights, and collaborating across teams.

Design and develop scalable data pipelines using PySpark and Databricks, focusing on data ingestion, transformation, validation, and performance optimization.