Neural Nexus â?? Data Steward, Data & AI Enablement (AuRA)

**What You Will Do**

Lets do this. Lets change the world.

In this vital role within Neural Nexus AI Data Enablement (AuRA), you will be responsible for the execution and delivery of AI-enabled data foundations and data products that power commercial analytics and AI use cases across Amgen.

This is a hands-on execution and delivery leadership role, focused on building, operationalizing, and managing scalable, governed, AI-ready data solutions. You will own delivery of assigned workstreams end-to-end, ensuring solutions are implemented on time, meet quality standards, and drive measurable business value.

You will also play a key role in ensuring data stewardship, governance, and FAIR-aligned data practices, enabling trusted and reusable data assets across AuRA.

**Key Responsibilities**
1. End-to-End Delivery of Data & AI Enablement Workstreams

• Lead execution of AuRA data enablement workstreams from intake, design, and development through deployment and operationalization.
• Deliver AI-enabled data products and pipelines that support commercial analytics and downstream applications.
• Ensure solutions are delivered on time, within scope, and aligned to business requirements.
• Coordinate readiness activities including UAT, access provisioning, documentation, and production handoff.

2. Data Platform Implementation & AI Readiness

• Implement and scale AI-ready data foundations within AuRA, integrating structured and unstructured data sources.
• Build and manage reliable, scalable data pipelines with appropriate monitoring, logging, and versioning.
• Support development of reusable capabilities such as:
  • Data models and curated data layers
  • Metadata, tagging, and taxonomy frameworks
  • Data discoverability and access enablement
• Ensure data is optimized for performance, usability, and downstream analytics/AI consumption.

3. Data Governance, Stewardship & Quality

• Enforce data governance practices including:
  • Data ownership and stewardship
  • Data lineage and traceability
  • Role-based access controls
• Ensure adherence to FAIR principles (Findable, Accessible, Interoperable, Reusable) and Clean Core alignment.
• Drive data quality processes (QC, MDM, affiliation) to ensure data is business-ready and trusted.

4. Program Execution & Workstream Management

• Manage multiple concurrent workstreams with clear ownership of:
  • Scope, timelines, risks, and dependencies
• Drive structured program execution using agile and project management best practices.
• Identify and proactively mitigate delivery risks and bottlenecks.
• Track and report on delivery health, milestones, and progress.

5. Cross-Functional Collaboration

• Partner with Commercial, CD&A, Analytics, Technology, and Engineering teams to deliver data solutions aligned to business priorities.
• Act as a bridge between business stakeholders and technical teams, ensuring clarity in requirements and execution.
• Coordinate across teams to maintain execution alignment and delivery velocity.

6. Adoption & Value Realization

• Enable business and analytics teams to access and use trusted AuRA data products.
• Support onboarding activities including:
  • Training sessions
  • Documentation and data product guides
  • Working sessions with end users
• Track adoption, usage, and value realization metrics to measure impact.

7. Continuous Improvement & Change Management

• Support change management by ensuring readiness of processes, documentation, and communication.
• Gather feedback from users and stakeholders to improve data products and delivery processes.
• Contribute to building repeatable, scalable delivery frameworks for AuRA data enablement.

**What We Expect of You**
Preferred Qualifications

• Strong experience in data platform implementation and data enablement delivery
• Experience working with commercial pharma datasets (CRM, claims, HCP/HCO, patient data) preferred
• Solid understanding of:
  • Data pipelines (ETL/ELT), data modeling, and data integration
  • Data governance, MDM, affiliation, and data quality frameworks
  • AI/ML data requirements and data readiness
• Experience with modern data platforms (e.g., Snowflake, Databricks, Airflow) and cloud environments
• Proven ability to manage multiple concurrent workstreams and complex dependencies
• Strong prog…