• Lead the creation & development of a standard library of reporting and/or analysis solutions (report, dashboard, visualization, or other) for Data Sciences and other stakeholders. • Gather requirements from a cross-functional group to a particular study question or operational activity. • Build & configure solution to meet the requirements. • Lead validation activities to prepare reporting solutions for release/usage. • Gather (as needed) and collaborate with volunteer stakeholder group to support validation activities. • Contribute to the Data Standards Governance Committee (DSGC) for the management of standard reports. • Maintain ongoing updates to standard report templates. • Support training for the Data Sciences team of new standard reports/solutions. • Deploy standard reporting solutions for use on a study-specific basis. • Create & develop ad hoc reporting and/or analysis solutions – report, dashboard, visualization, or other – as requested by study team members. • Gather requirements from study team member(s) for ad hoc solutions. • Build & configure ad hoc solutions to meet the requirements. • Support report requestor in validation activities of study-specific ad hoc solutions to prepare for release/usage. • Support long-term maintenance of study-specific solutions and contribute to investigating issues, as raised by study team members. • Identify requirements with Data Sciences leaders to measure & monitor operational and performance metrics. • Design, build, and maintain metrics governance within Data Sciences. • Work with large data sets to analyze operational & study data and deliver reporting that outline areas for process & performance improvement/optimization (inefficiencies, inaccuracies, and other issues) to Data Sciences leadership. • Participate in the strategy, management and integration of data reporting systems that impact Data Sciences, including the assessment, integration, build of vendor systems and Alimentiv technologies. • Act as a technical system subject matter expert; perform validation and testing of systems. • Help gather and determine requirements for relevant technical systems, with cross-functional groups or other Business/Process analysts. Work closely with other technical roles (Data Management, Database Programming, Statistics, Enterprise Analyses, etc.) to drive data governance and excellence. • Assess ways to further leverage existing systems to optimize internal efficiencies and processes. • Support Data Sciences contribution to a Data Warehouse, contributing to key decisions and strategy. • While evaluating new projects or requests for enhancements, help determine the project cost, timeline, goals and feasibility and then support the project through completion.

• Lead the creation & development of a standard library of reporting and/or analysis solutions for Data Sciences and other stakeholders. • Gather requirements from a cross-functional group to a particular study question or operational activity. • Build & configure solution to meet the requirements. • Lead validation activities to prepare reporting solutions for release/usage. • Gather (as needed) and collaborate with volunteer stakeholder group to support validation activities. • Deploy standard reporting solutions for use on a study-specific basis. • Create & develop ad hoc reporting and/or analysis solutions – report, dashboard, visualization, or other – as requested by study team members. • Identify requirements with Data Sciences leaders to measure & monitor operational and performance metrics.

• Collaborate with cross-functional stakeholders to develop scalable analytics solutions • Create intuitive dashboards and visualizations • Deliver reporting that improves operational and business performance • Transform complex data into actionable insights that drive strategic decision-making • Analyze complex datasets to identify trends, opportunities, and actionable business insights • Design, build, deploy, and maintain reporting and dashboards for internal users and external customers • Support development and execution of the BI roadmap aligned to business priorities • Ensure data quality and integrity across data warehouse and BI platforms • Proactively identify and resolve scalability, performance, and optimization issues

• Define, report, and monitor key performance indicators of product areas, and lead deep dives into variations of critical metrics from normal behavior • Use Python and write SQL queries to improve data pipeline and generate reports • Build data visualizations using Metabase and Jupyter notebooks as a method of conveying data insights • Help engineering teams set up tests and perform statistical analysis of experiments • Convert analytical insights into concrete, meaningful recommendations for business or product improvement, and succinctly communicate these findings to various partners and leadership