Kite Data & Integration Lead - Sales & Digital

At Gilead, we’re creating a healthier world for all people. For more than 35 years, we’ve tackled diseases such as HIV, viral hepatitis, COVID-19 and cancer – working relentlessly to develop therapies that help improve lives and to ensure access to these therapies across the globe. We continue to fight against the world’s biggest health challenges, and our mission requires collaboration, determination and a relentless drive to make a difference.
 

Every member of Gilead’s team plays a critical role in the discovery and development of life-changing scientific innovations. Our employees are our greatest asset as we work to achieve our bold ambitions, and we’re looking for the next wave of passionate and ambitious people ready to make a direct impact.
 

We believe every employee deserves a great leader. People Leaders are the cornerstone to the employee experience at Gilead and Kite. As a people leader now or in the future, you are the key driver in evolving our culture and creating an environment where every employee feels included, developed and empowered to fulfil their aspirations. Join Gilead and help create possible, together.

Job Description
 

The Kite Data & Integration Lead - Sales & Digital is an incredible opportunity within the IT Data & AI CoE team to support the transformation of AI/ML for Kite’s Commercial line of business.

As part of our enterprise AI strategy to accelerate life‑changing therapies to patients, we are seeking a Data/AI Product Owner to drive the AI/data product strategy for Commercial Reporting & Analytics. Serving as the primary liaison between GDDI, IT and partners teams to build production-grade analytics infrastructure on AWS and Databricks. You will translate ambiguous and evolving Commercial needs into a clear, prioritized product roadmap, define success metrics tied to measurable business outcomes, and influence both near‑term delivery and longer‑term data and AI platform evolution. As the IT product owner, you will own the product vision, roadmap, and stakeholder engagement for AI‑enabled reporting, planning, and analytics use cases, while maintaining a strong understanding of the technical architecture required to deliver these solutions.

Key Responsibilities:

ML & Data Engineering

• Model Lifecycle Management: Develop and maintain pipelines to transition models from experimentation to production, including packaging, CI/CD, automated testing, and deployment. Support model serving for patient identification, alignment prediction, next-best-action engines, and competitive intelligence models.

• Data Pipeline Development: Design robust batch and streaming data workflows; integrate, define, and manage feature sets, lineage, and reuse to support.

• Production Operations & Monitoring: Ensure reliability and scalability of data pipelines; implement effective logging, tracing, and alerting. Establish monitoring for model performance, data drift, bias, and service health. Monitor data quality across rare disease data feeds, where small population sizes amplify the impact of anomalies.

Agentic AI & Agent Systems Engineering

• Agent Workflow Development: Collaborate with data scientists and commercial stakeholders to decompose complex business workflows into agent-executable workstreams. Define boundaries between agent execution and human data science judgment.

• Instruction Architecture & Prompt Engineering: Design and maintain prompt architectures, agent skills, memories, and context injection patterns. Author structured coding instructions that translate commercial analytics requirements into precise agent directives with clear acceptance criteria.

• Token Economics & Cost Optimization: Optimize agent execution for cost efficiency—manage context window utilization, minimize token consumption, and design instruction patterns that reduce iteration cycles. Monitor token economics per workstream to balance capability with budget.

Governance, Security & Compliance

• Model, Agent & Data Governance: Implement version control, approvals, documentation, and audit trails for datasets, code, models, and agent instructions. Ensure all AI/ML outputs are explainable, auditable, and compliant with HIPAA/PHI, GDPR, FDA promotional regulations, and REMS requirements. Enforce secrets management, role-based access control, network policies, and data protection for agents operating on sensitive healthcare and commercial data within the e