Assoc Director, IT Data Engineering

Gilead Sciences•Raleigh, NC

21h•Hybrid

About The Position

At Gilead, we are seeking an Associate Director, AI Lead (D&A Digital Marketing) to spearhead the technical development and delivery of AI/ML and Generative AI solutions for Commercial Digital Marketing. This role involves bridging business needs with technical execution, managing internal and external delivery teams, and ensuring technical excellence through architectural decisions, code quality standards, and advanced analytics methodologies. The objective is to deliver measurable impact from concept to scaled release, aligning with Gilead's enterprise AI strategy to accelerate life-changing therapies for patients. This position reports to the Director, Data/Analytics/AI (D&A Digital Marketing) and is based in Raleigh, NC, with hybrid eligibility. As a People Leader, this role also involves fostering an inclusive environment, developing talent, and empowering teams to achieve organizational objectives.

Requirements

Bachelor’s degree with 10+ years or Master’s degree with 8+ years of relevant experience delivering AI solutions in data science, computer science, engineering, or a related field (or equivalent combination of education and experience).
Significant experience leading technical delivery in an Agile environment, including backlog prioritization, sprint planning and release execution.
Hands on experience as a lead data scientist, AI/ML engineer, data engineer or solution architect delivering production solutions.
Strong understanding of ML methods, statistics, model validation and evaluation.
Hands on Generative AI experience, including prompt engineering and LLM evaluation methods used to field enterprise solutions.
Experience with multi‑model agentic AI (model selection/routing, tool routing, prompt evaluations/benchmarks and production observability).
Practical understanding of MLOps and LLMops practices for production operations, monitoring and lifecycle management.
Experience delivering solutions on modern cloud and data platforms, including AWS, Databricks, and development with Python-based tooling.
Strong stakeholder leadership skills with ability to influence without authority and communicate clearly with cross functional teams.
Product centric mindset and ability to operate effectively in ambiguity with continuous learning and iteration.
Minimum Required Education and Years of Experience: Bachelor's Degree and Ten Years’ Experience OR Masters' Degree and Eight Years’ Experience OR PhD and Two Years’ Experience.

Responsibilities

Own end to end delivery of US Commercial Digital Marketing AI solutions from ideation through production, including timeline, technical quality and release readiness.
Apply a product centric approach with clear ownership, roadmaps and user adoption metrics for each AI capability delivered.
Oversee development of scalable solutions using AWS and Databricks and approved Commercial enterprise platforms.
Build, train or fine tune ML models for business problems, validate and evaluate them for fielding as part of broader solutions.
Deliver Generative AI solutions, including prompt engineering and retrieval/grounding patterns, and apply quantitative and qualitative evaluation before scaling to production.
Design AI assistants or agent workflows where they improve speed, quality and consistency of insights and recommendations.
Design and deliver agentic AI solutions, including multi‑agent orchestration and intelligent routing of requests across agents/tools/models, with clear evaluation and guardrails.
Establish measurement frameworks to track business impact and model performance using KPIs and experimentation methods where applicable.
Build reusable AI components (templates, evaluation approaches, shared patterns) and promote reuse to accelerate future delivery.
Partner with engineering teams to align to MLOps and LLMops expectations for deployment, monitoring, lifecycle updates and controlled releases.
Embed responsible AI, privacy and compliance by partnering with legal, privacy and ethics teams to address data privacy, transparency and risk management.
Manage vendor relationships and co sourced delivery partners, ensuring delivery quality, documentation and continuity.
Communicate AI progress, outcomes, limitations and risks clearly to stakeholders and act as a translator between technical and non-technical audiences.