• Partners with scientists, engineers, and community collaborators to curate and coordinate multimodal biomedical datasets. • Guide researchers through data ingestion into data hubs. • Support the development of data models and annotations for specialized biomedical data types. • Contribute to data governance and workflow design that enable discovery-driven science. • Apply metadata standards and contribute to improving data management plans across research projects. • Independently manage datasets and ensures appropriate metadata documentation and lifecycle management. • Identify and resolve inconsistencies to validate datasets and metadata and ensure research readiness. • Apply tools and automation techniques, including AI to improve efficiency of data management tasks such as metadata extraction, validation, and documentation. • Coordinate with engineering, governance, and scientific teams to implement consistent data standards across projects. • Provide guidance on data-sharing tools, standards, and best practices for contributors.

• Support data validation across historical and newly ingested datasets. • Analyze large datasets to identify trends, patterns, and feature relevance. • Assist with data preparation, cleansing, and transformation for modeling. • Collaborate with the Senior Data Scientist on feature understanding and engineering. • Evaluate data across multiple data zones to ensure consistency and accuracy. • Help refine predictive outputs to improve demand and pricing forecasts. • Document findings, assumptions, and validation results throughout the PoC.

• Partner with product, marketing, and operations teams to turn business needs into analytical solutions • Analyze large-scale customer and product data to uncover patterns, trends, and opportunities • Develop and refine customer segmentation models to support targeted marketing and personalization • Build and improve recommendation systems (collaborative, content-based, or hybrid) • Select, train, and evaluate machine-learning models with a strong focus on business outcomes • Define and track performance metrics for personalization and recommendation use cases • Support data quality, enrichment, and modeling during our migration to PIM and CDP platforms • Present insights and recommendations to stakeholders in a clear and actionable way

• Carefully parsing assigned machine learning papers or benchmark leaderboards and identifying key information. • Accurately extracting and compiling these technical details. • Producing informed estimates of metrics about the physical infrastructure supporting AI based on limited information. • Gathering data on the inputs, impacts, and details of AI, as well as carrying out analysis of the gathered data. • Reporting on and discussing findings with your supervisor.