Senior Analyst

Position Summary

Key Responsibilities

• Lead the design and execution of complex analytical projects from problem definition through delivery, including multi-source data integration, longitudinal analysis, statistical modeling, and geospatial analysis.
• Translate ambiguous, high-stakes business and policy questions into rigorous analytical frameworks, ensuring methodological soundness, interpretive clarity, and defensible findings.
• Apply advanced statistical techniques including regression modeling, hypothesis testing, time-series analysis, clustering, and cohort analysis to produce high-quality, reproducible analytical outputs.
• Develop and maintain sophisticated dashboards, analytical pipelines, and reporting systems that deliver ongoing intelligence to clients and internal stakeholders.
• Synthesize findings from complex, large-scale datasets into clear executive narratives, visualizations, and actionable recommendations tailored to technical and non-technical audiences.
• Validate and quality-assure analytical work produced by junior analysts, ensuring accuracy, consistency, and appropriate handling of data quality issues and analytical limitations.

• Serve as a senior analytical point of contact for assigned client engagements, building trusted relationships with program managers, data teams, and senior client stakeholders.
• Lead client-facing analytical briefings, data review sessions, and findings presentations, communicating complex results with confidence and clarity.
• Translate evolving client needs and feedback into analytical adjustments, scope refinements, or new workstreams in coordination with project managers and technical leads.
• Support account managers and project managers in identifying analytical expansion opportunities within existing client accounts by staying attuned to unmet client data needs.
• Contribute to client onboarding and data discovery processes, helping clients understand their data landscape and how RS21's analytical capabilities can address their most pressing challenges.

• Serve as an individual contributor on complex and high-priority analytical deliverables, applying senior-level expertise directly to client work rather than delegating core analytical tasks.
• Write clean, well-documented, and reproducible analytical code in Python, R, or SQL, contributing directly to analytical pipelines, exploratory analysis, and production-ready reporting.
• Take ownership of end-to-end analytical workstreams including data extraction, cleaning, transformation, analysis, and visualization, ensuring high standards at every stage.
• Stay current with evolving tools, statistical methods, and visualization best practices within the data analytics field, bringing relevant innovations into RS21's day-to-day analytical practice.
• Apply domain knowledge across RS21's sectors, including healthcare data structures, government reporting frameworks, or utility operational data, to deliver contextually grounded and credible analytical work.

• Provide technical mentorship and day-to-day guidance to junior and core analysts, supporting their growth in analytical methods, communication, and professional effectiveness.
• Review and quality-assure analytical deliverables produced by less experienced team members, providing constructive, specific feedback that raises the overall quality bar of the practice.
• Contribute to onboarding of new analysts by sharing RS21's tools, standards, documentation practices, and client engagement expectations.
• Participate in or lead internal knowledge-sharing sessions, peer learning events, and analytical communities of practice that build collective team capability.
• Model the analytical standards, client orientation, and professional behaviors expected of all members of the Data Analytics team.

• Contribute to proposals and business development efforts by drafting technical approach narratives, analytical methodology descriptions, and relevant past performance documentation.
• Participate in client discovery conversations and pre-award technical discussions, representing RS21's analytical capabilities with credibility and specificity.
• Document and share project lessons learned, analytical frameworks, and reusable methodologies that strengthen RS21's institutional capability and proposal quality over time.

Qualifications

• Bachelor's degree in Statistics, Mathematics, Economics, Computer Science, Public Policy, Public Health, or a related quantitative field. Master's degree preferred.
• 6 to 10 years of progressive experience in data analysis, business intelligence, or applied analytics, with a demonstrated track record of independently leading complex analytical projects.
• Expert-level proficiency in SQL and at least one analytical programming language, Python or R, including experience with libraries for data manipulation, statistical analysis, and visualization.
• Advanced proficiency with data visualization and business intelligence platforms such as Tableau, Power BI, Looker, or equivalent.
• Strong grounding in applied statistics, including regression, significance testing, sampling methodology, and uncertainty communication.
• Demonstrated experience presenting analytical findings to senior client stakeholders and translating technical outputs into clear, decision-ready narratives.
• Experience working with messy, incomplete, or multi-source datasets common in government, healthcare, or regulated industry contexts.
• Demonstrated experience mentoring analysts and contributing to team quality and capability development.

• Master's degree in a quantitative discipline such as Statistics, Applied Mathematics, Data Science, Public Health, or a related field.
• Experience working with federal, state, or local government clients, including familiarity with government data standards, privacy regulations (e.g., HIPAA, FERPA), and reporting requirements.
• Background in healthcare analytics, population health, program evaluation, or social services data.
• Experience with geospatial analysis tools such as ArcGIS, QGIS, or Python-based spatial libraries (e.g., GeoPandas, Folium).
• Familiarity with cloud-based data platforms such as AWS Redshift, Google BigQuery, Snowflake, or Azure Synapse.
• Experience with longitudinal data, panel data methods, or quasi-experimental evaluation designs.
• Familiarity with dbt, Airflow, or other data pipeline and transformation tooling used in collaboration with data engineering teams.