• Design and analyze experiments to improve revenue • Classify audience and content to support programmatic direct and indirect sales • Deliver evolutionary floor price guidance • Visualize complex systems • Build deep understanding of data generating processes

• Identifying and qualifying leads for AI/ML projects across various industries. • Architect and develop end-to-end AI/ML solutions tailored to complex business challenges • Leading client engagements from initial contact through project scoping and proposal development • Lead technical design and implementation of production-grade models and pipelines. • Own the deployment, optimization, and monitoring of models and infrastructure (MLOps). • Collaborate across teams to ensure technical excellence and project success. • Mentor peers and contribute to knowledge-sharing across the organization. • Driving sales strategies and achieving revenue targets. • Building long-term relationships with clients and partners.

• Participating in the design and development of AI solutions for challenging projects. • Building production level ML/AI solutions, with solid software engineering and ML/AI principles. • MLOps Automated deployment and monitoring (models and infrastructure). • Data analysis (data cleaning, variable transformation, etc.). • Developing and training ML models. • Putting AI models into production. • This means parallelizing, optimizing, tuning, testing the models to deploy in a production environment.

• 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.