Scientific Leader, AI-Powered Discovery Systems

GlaxoSmithKline; Upper Providence (Delaware County); Hybrid; USD 150,000 - 190,000; Full time

Job summary

GlaxoSmithKline (GSK) invites a Scientific Leader, Discovery Integration Sciences to help build the world’s leading LIAL platform. You will steer cross‑functional teams of scientists and engineers to design, implement, and operate integrated discovery systems that fuse computational intelligence with automated lab workflows.

You will mentor early career researchers, translate scientific needs into robust system architectures, and push AI-driven data workflows across cloud environments to

Qualifications

• PhD or MS with 3+ years in a scientific field with computational work.
• Minimum 5 years leading matrix teams across science and tech to deliver software in evolving environments.
• Minimum 5 years Python (or R) programming experience.
• Experience with modern software development tools (OOP, git/GitHub, CI/CD).
• At least 3 years cloud experience (GCP, AWS, Azure, Kubernetes) for data workflows and lab automation.

Responsibilities

• Collaborate across R&D and Tech to design and deploy LIAL Systems.
• Translate science requirements into system designs and enable platform capabilities.
• Lead cross-functional teams, mentoring others.
• Apply new methods and data sources to accelerate autonomous science using AI.
• Prepare presentations and decision materials for project forums.

Skills

Python programming

Leadership

Cross‑functional teams

AI/ML concepts

Data analysis

Education

PhD or MS in a relevant scientific discipline

Tools

Git/GitHub

CI/CD

Nextflow

Airflow

Kubernetes

Job description

GlaxoSmithKline (GSK) invites a Scientific Leader, Discovery Integration Sciences to help build the world’s leading LIAL platform. You will steer cross‑functional teams of scientists and engineers to design, implement, and operate integrated discovery systems that fuse computational intelligence with automated lab workflows.

You will mentor early career researchers, translate scientific needs into robust system architectures, and push AI-driven data workflows across cloud environments to