LexisNexis Legal & Professional is a leading global provider of legal, regulatory and business information and analytics that help customers increase productivity, improve decision-making and outcomes, and advance the rule of law around the world. As a digital pioneer, the company was the first to bring legal and business information online with its Lexis® ...
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The Senior Data Analyst I works closely with business partners to understand needs, clarify problems, and apply data-driven approaches to inform decisions. This role develops insights, builds analytical solutions, creates clear visualizations, and communicates findings in an accessible way. The Senior Data Analyst I independently leads complex analytics projects and supports the growth of junior team members.
Responsibilities
Collaborate with stakeholders to understand business goals and identify meaningful analyses, metrics, and success measures.
Help define measurable outcomes aligned with organizational objectives.
Prepare, transform, and validate data, including work with large or complex datasets.
Apply statistical methods and analytical modeling to assess trends, drivers, and potential outcomes.
Develop clear visualizations and data stories tailored to diverse audiences.
Lead analytics projects end-to-end, including scoping, planning, and managing timelines.
Serve as a subject matter expert in assigned areas and provide mentorship to junior analysts.
Build understanding of related disciplines to improve analysis and interpretation.
Use approved AI tools to streamline work—such as drafting analyses, summarizing findings, generating code, or documenting workflows—while ensuring accuracy and quality.
Create reusable data products that support users at varying levels of data literacy (e.g., metric bots, guided dashboards).
Requirements
Bachelor’s or master’s degree in data Analytics, Data Science, Mathematics.
3–5 years of experience in analytics, reporting, or data-focused roles.
Strong ability to understand complex data structures and apply advanced data preparation and quality techniques.
Experience using intermediate statistical methods for business analysis.
Proficiency with SQL and Python for querying, transformation, and analysis.
Experience with data visualization tools (e.g., Tableau, Power BI) and designing dashboards for business users.
Familiarity with modern analytics platforms (e.g., Databricks or similar).
Strong project execution skills, including the ability to organize work, manage multiple priorities, communicate progress, and address obstacles.
Ability to craft effective prompts for AI tools and refine outputs using domain knowledge.
Clear communication skills, with the ability to explain technical concepts to non-technical audiences.
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