Hybrid Scientific Leader — Discovery AI Systems

GSK; Melbourne; Hybrid; AUD 200,000 - 280,000; Full time

Job summary

GSK in Melbourne is seeking a Scientific Leader in Discovery Integration Sciences to drive the System-of-LIAL-Systems powering research activities and the LIAL Platform.

You will lead cross-functional teams across R&D and Tech to design, deploy, and operate integrated, automated discovery systems, blending computational intelligence with laboratory experimentation in adaptive learning cycles.

Qualifications

• Doctoral degree (PhD) or MS +3 years in a relevant scientific discipline with computational focus.
• Minimum 5 years leading matrix teams across disciplines to deliver software projects integrating wet and dry lab capabilities.
• Minimum 5 years programming with Python (preferred) and/or R.
• Experience with modern software development tools (OOP, git/GitHub, CI/CD).
• Minimum 3 years experience using cloud technologies (GCP/AWS/Azure/Kubernetes) for data workflow automation and digital lab automation.

Responsibilities

• Collaboratively develop and deploy LIAL Systems by partnering with scientists and technical teams to redesign experimental processes.
• Translate scientific requirements into system designs and enabling platform capabilities.
• Foster inclusive collaboration by leading cross-functional matrix teams and mentoring others.
• Apply new methods, tools, and data sources to drive innovation and improve autonomous science using AI and agents.
• Prepare clear presentations and reports for project teams and decision forums.

Skills

Python programming

Team leadership

Software development

Education

PhD or MS +3 years

Tools

Git / GitHub

CI/CD

Nextflow

Airflow

Kubernetes

Job description

GSK in Melbourne is seeking a Scientific Leader in Discovery Integration Sciences to drive the System-of-LIAL-Systems powering research activities and the LIAL Platform.

You will lead cross-functional teams across R&D and Tech to design, deploy, and operate integrated, automated discovery systems, blending computational intelligence with laboratory experimentation in adaptive learning cycles.