• Celestar, a B&A Company, is looking for a Data Governance & Metadata Scientist in support of an important mission. • In this role, you will have the opportunity to work with a great team while supporting SOUTHCOM. • Develop, implement, and maintain data governance frameworks. • Ensure metadata consistency, lineage tracking, and interoperability between Data Lake, ESRI Kubernetes environments, and external data sources.

• Work with large and complex data sets to solve a wide array of challenging problems using different analytical and statistical approaches. • Apply technical expertise with quantitative analysis, experimentation, data mining, forecasting and the presentation of data to identify trends and patterns that can inform decision-making. • Collaborate closely with Product, Engineering, Finance, Marketing, Operations, Data Science, Advanced Analytics, Data and Analytical Engineering, Capacity Planning and other cross-functional teams to provide actionable insights. • Design, execute, and analyze experiments to measure feature impact and guide product iterations. Leverage causal methods when traditional randomized trials aren’t available. • Design and build metrics / dimensions to monitor product and business performance. • Build sophisticated reporting and analytical tools that empower stakeholders to make data-driven decisions. • Present findings and recommendations to stakeholders, including senior leadership, in a clear and concise manner. • Stay up-to-date with the latest industry trends and advancements in quantitative and qualitative data analysis techniques. • Leverage AI to automate repetitive tasks and empower stakeholders.

• Design and implement automated data flows to integrate data and processes into the contracting authority’s systems and databases (historical data migration and new ETL flows); • Performance monitoring and improvement of existing and new ETL flows; • Ensure data quality is properly defined, documented, analysed and monitored; • Support data analysis, modelling and visualisation in close collaboration with internal stakeholders, such as disease surveillance experts and fellow data managers.

• Build, train, and deploy machine learning models and data pipelines using Python, pandas, NumPy, and scikit-learn, ensuring scalability and reliability in production. • Design, implement, and continuously improve LLM-powered applications and Retrieval-Augmented Generation (RAG) pipelines, including prompt engineering, embedding strategies, vector store integration, and evaluation frameworks. • Build and iterate on agentic AI systems that leverage tool use, multi-step reasoning, and orchestration frameworks to automate complex workflows and decision-making processes. • Conduct exploratory data analysis (EDA) to uncover trends, patterns, and anomalies that inform business decisions and product strategy. • Design and build data visualizations and dashboards that communicate key metrics and insights to both technical and non-technical stakeholders. • Diagnose and resolve complex data and model issues, minimizing drift and continuously improving pipeline efficiency and model accuracy. • Drive technical excellence through rigorous model evaluation, identifying opportunities for improvement, and enforcing best practices in code quality, experiment tracking, and reproducibility. • Collaborate closely with cross-functional teams to translate business requirements into data-driven insights, models, and high-quality analytical solutions.