• Play a critical role in advancing Seed’s data capabilities • Partner closely with stakeholders across Product, Science, Engineering, Marketing, and Operations • Transform data into actionable insights that drive business growth and scientific discovery • Contribute to scalable frameworks and help foster a data-driven decision-making culture • Develop and scale analytics capabilities in alignment with company priorities • Design analyses, reports, and dashboards that inform strategy and execution • Build scalable pipelines and data models that support self-service analytics • Collaborate on designing and interpreting experiments • Maintain processes for data quality, accuracy, and consistency • Collaborate on data architecture and performance • Provide insights that optimize acquisition, retention, and customer experience • Support with data-driven approaches to study design and analysis • Identify opportunities for innovation and efficiency through advanced analytics

• Optimize and expand existing PostgreSQL-based analytics pipelines, ensuring performance, scalability, and data accuracy across large operational datasets. • Extend existing analytical models and reports to incorporate new data sources, asset types, or mission areas under the government contract. • Develop interactive dashboards, reports, and data visualizations using business intelligence tools leveraging custom SQL as the foundation for all datasets. • Analyze industrial asset performance metrics – including operational availability, reliability, and maintenance success rates – to identify trends and improvement opportunities. • Support the development and continuous improvement of key performance indicators (KPIs) that measure maintenance performance, asset reliability, and lifecycle effectiveness. • Extract, transform, and analyze data from ERP and CMMS systems. • Perform data validation and continuous quality assurance to ensure consistency and accuracy across reports and source systems. • Collaborate with stakeholders to understand reporting requirements and translate business needs into technical solutions. • Ensure data quality, integrity, and security in compliance with applicable government standards. • Document analytical processes, methodologies, and SQL logic for traceability and reproducibility. • Support ad-hoc data analysis requests and provide actionable insights to leadership. • Contribute to the enhancement of established data workflows and reporting standards to improve scalability, consistency, and usability across contracts.

• Design and build data pipelines that connect to source systems, transform raw data into audit-ready datasets, and enable full-population testing across entities and jurisdictions. • Develop automated test scripts, anomaly detection models, and risk scoring mechanisms that replace manual sampling with continuous, data-driven assurance. • Validate the completeness and accuracy of data used in analytics — you don’t just visualize data, you first prove it matches the source system. • Leverage AI-assisted tools to enhance analytics workflows, from accelerating data extraction and transformation to building smarter detection models. • Partner with SOX testers and operational auditors to translate audit objectives into scalable analytical tests. • Identify opportunities to leverage emerging technologies, including AI-assisted tools, to enhance coverage and efficiency. • Build and maintain dashboards and visualizations that provide Internal Audit and senior leadership with real-time insights into control effectiveness, exception trends, testing coverage, and audit findings. • Design continuous monitoring solutions that shift Internal Audit from periodic, point-in-time testing to ongoing surveillance of control health across critical processes. • Work with Engineering, Data, and Business Operations teams to identify data sources, understand data lineage, and secure timely access to the data needed for audit analytics. • Champion a data-first mindset within Internal Audit. Train auditors on analytics tools, build self-service datasets and documentation, and empower non-technical team members to explore data independently. • Communicate complex data analytical findings clearly to both technical and non-technical audiences, translating data into actionable audit conclusions for senior leadership and the Audit Committee.

• Collect, compile, and integrate media data from multiple sources, including digital platforms, offline media agencies, and internal systems. • Harmonize multi-source datasets across taxonomy, granularity, and timeframes to create structured, analytics-ready data. • Clean, normalize, and de-duplicate datasets to establish a reliable single source of truth for media analytics. • Identify data gaps, inconsistencies, and KPI misalignments, and apply validation checks to ensure data accuracy and completeness. • Build and maintain automated data preparation pipelines using Python and advanced Excel capabilities such as Power Query and Pivot Tables. • Collaborate closely with media, analytics, and measurement teams to ensure data structures align with analytical and business objectives. • Clearly communicate media data concepts and metrics to both technical and non-technical stakeholders.