Responsibilities
- Source, obtain, and organize commercial science and technology datasets including academic publications, patents, grants, startup investments, corporate profiles, and informal technical literature.
- Collaborate directly with expert analysts to respond to strategic inquiries, automate reporting, and build dashboards that deliver live insights into international research developments.
- Implement a comprehensive data quality initiative covering data profiling, cleaning, enhancement, and stewardship practices.
- Keep accurate records of metadata, data lineage, and data definitions using enterprise-level data management tools.
- Create and roll out advanced analytical tools, interactive dashboards, and artificial intelligence or machine learning models in coordination with program stakeholders.
- Deliver onboarding, training, and continuous technical support for analytics platforms to promote data fluency across teams.
- Lead the formation and operation of a Data Governance Board responsible for setting data policies, standards, and compliance protocols.
- Sustain ongoing communication with end users and leadership to enable continuous refinement of data systems.
- Design, deploy, and manage all data infrastructure and applications within a cloud environment compliant with FedRAMP High and DoD Impact Level 5 requirements.
- Ensure uninterrupted Authority to Operate by managing system configurations, applying security updates, monitoring performance, and providing help desk services.
- Architect systems with scalability in mind to support growing data volumes and increasing user demand.
- Deploy and maintain secure methods for data access, including APIs, exploration interfaces, and controlled bulk download capabilities for approved personnel.
- Oversee all data access systems under a formal governance structure, including metadata controls and data cataloging procedures.
- Design and implement machine learning and artificial intelligence models, including natural language processing for analyzing unstructured text.
- Integrate AI/ML platforms such as AWS SageMaker and Azure Machine Learning into existing workflows.
- Automate infrastructure provisioning and operational processes using Infrastructure as Code tools like Terraform and Ansible.
- Develop and maintain CI/CD pipelines to support secure and reliable software deployment.
- Construct and operate real-time data processing pipelines using technologies such as Apache Kafka or AWS Kinesis.
