Associate Director, Data Scientist

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 Responsibilities: AI Operations, Contractor Delivery & Hands-On Technical Work Works as part of a team responsible for managing contractors, technical delivery partners, and AI workstreams across applied AI initiatives. Supports contractor onboarding, work planning, technical direction, delivery coordination, quality review, and accountability for assigned work. Provides hands-on technical direction for AI prototypes, model development, application patterns, data pipelines, and production AI systems. Reviews technical designs, architecture decisions, model evaluation plans, code quality, implementation tradeoffs, and production-readiness. Contributes to prototypes, proof-of-concepts, notebooks, design documents, technical spikes, and code reviews when needed. Promotes scientific rigor, reproducibility, engineering excellence, responsible AI practices, documentation, and maintainable delivery patterns. AI Research, Applied Innovation & Product Value Leads development, evaluation, deployment, and scaling of AI capabilities supporting research, development, clinical, regulatory, safety, and enterprise use cases. Applies product thinking to ensure AI solutions address clear user needs, workflow realities, business priorities, adoption goals, and measurable outcomes. Partners with Product Management & Experiences to understand user needs, prioritize opportunities, define success metrics, and support adoption. Uses experimentation, user feedback, benchmarking, and iterative delivery to validate assumptions and improve AI capabilities over time. Identifies opportunities to use emerging AI technologies to accelerate scientific discovery and operational effectiveness. Uses experimentation, user feedback, benchmarking, and iterative delivery to validate assumptions and improve AI capabilities over time. Identifies opportunities to use emerging AI technologies to accelerate scientific discovery and operational effectiveness. Technical Architecture & Engineering Excellence Designs and guides AI solution architectures for assigned projects and business domains. Guides development of Retrieval-Augmented Generation systems, agentic workflows, prompt and context engineering patterns, evaluation harnesses, model monitoring, and Langfuse-based observability. Sets expectations for production-quality code, automated testing, version control, reproducible experiments, scalable deployment patterns, and operational documentation. Develops reusable AI frameworks, tools, accelerators, platforms, and services that enable faster delivery across Research, Development, and enterprise functions. Helps troubleshoot complex issues across data quality, model behavior, latency, reliability, security, scalability, cost, compliance, and user experience. Responsible AI, Governance & Production Operations Ensures AI solutions follow applicable governance, privacy, security, regulatory, and responsible AI expectations. Implements practical approaches for Large Language Model evaluation, groundedness assessment, hallucination risk management, traceability, and quality measurement. Uses platforms such as Langfuse or equivalent approved tooling for LLM tracing, debugging, prompt and response analysis, observability, evaluation workflows, and production monitoring. Supports Machine Learning Operations, Large Language Model Operations, continuous integration and delivery, model monitoring, observability, and operational support practices. Collaboration & Stakeholder Engagement Collaborates with Product Management & Experiences, Business Delivery Excellence, Enterprise AI & Governance Excellence, ARC translational AI teams, and Development partners. Partners with scientists, therapeutic area leaders, clinical teams, regulatory functions, Information Technology, Privacy, and Drug Development Systems to prioritize high-impact AI opportunities. Communicates technical concepts, product strategy, risks, tradeoffs, and delivery progress clearly to technical and non-technical audiences. Contributes to ARC initiatives that advance AI capabilities, governance, adoption, product innovation, and operational excellence. Requirements Minimum Education & Experience PhD in Computer Science, Artificial Intelligence, Machine Learning, Computational Biology, Statistics, Engineering, or related discipline with 4+ years of relevant industry experience. MS in a related discipline with 8+ years of relevant experience. BS in a related discipline with 10+ years of relevant experience. Demonstrated expertise in Artificial Intelligence, Machine Learning, Deep Learning, Natural Language Processing, Generative AI, or related disciplines. Experience guiding technical contributors, contractors, or cross-functional project teams, including technical direction, coaching, delivery oversight, and quality review. Hands-on experience building, evaluating, and deploying AI or machine learning solutions in applied research, product, or enterprise environments. Core Technical Requirements Strong hands-on programming skills in Python and practical experience with modern AI and machine learning libraries such as PyTorch, TensorFlow, or equivalent approved technologies. Experience in clinical trial operational data, real-world data, and supporting trial feasibility, site selection and forecasting. Experience building Generative AI applications with LangChain, LangGraph, Semantic Kernel, Microsoft Agent Framework, Langfuse, AWS-native AI services, Microsoft Azure services where appropriate, or equivalent approved enterprise technologies. Experience designing and implementing Retrieval-Augmented Generation systems, including chunking, embeddings, vector search, reranking, grounding, citation patterns, retrieval evaluation, and response quality measurement. Experience developing agentic AI workflows, tool-use patterns, orchestration approaches, guardrails, human-in-the-loop review models, prompt engineering, context engineering, and model evaluation techniques. Experience with APIs, microservices, notebooks, Git-based development, automated tests, containerization, deployment patterns, monitoring, and operational support for AI systems. Experience deploying AI solutions in regulated environments with appropriate governance, security, privacy, compliance, scalability, reliability, and responsible use controls. AI Domain Expertise Strong knowledge of Generative AI, Large Language Models, advanced analytics, and applied machine learning. Experience in one or more of the following areas: foundation models, multimodal AI, agentic AI systems, scientific machine learning, knowledge graphs, Retrieval-Augmented Generation, Natural Language Processing, or advanced deep learning. Experience designing evaluation frameworks for Large Language Models, including accuracy, groundedness, hallucination ri