Scientific Leader, Discovery Integration Sciences

GSK•Upper Providence, PA

4d•Hybrid

About The Position

The Data, Automation and Predictive Sciences (DAPS) function of GSK Research Technologies focuses on large-scale data generation, curation, analysis and prediction to deliver higher PTRS (Probability of Technical and Regulatory Success) assets and unlock upper quartile ambitions. Within DAPS, the Discovery Integration Sciences team is driving efforts across Research to build the world’s most efficient and effective biopharma discovery platform at GSK by building and deploying innovative Lab-in-an-Automated-Loop (LIAL) Systems that integrate computational intelligence with physical laboratory experimentation in adaptive learning cycles. Each LIAL System will deliver a domain leading scientific discovery solution that leverages and extends state-of-the-art scientific methods by engineering technologies to create integrated discovery systems that empower GSK research scientists with the latest tools. LIAL Systems will therefore be physical instantiations of GSK’s mission to unite science, technology and talent to get ahead of disease together. As a Scientific Leader in the Discovery Integration Sciences team you will work and lead across scientific and technical disciplines in cross-functional teams designing, delivering and operating the System-of-LIAL-Systems that will power GSK’s Research activities and the underlying LIAL Platform that provides the abstracted blueprint and technical services for building and operating LIAL Systems at scale.

Requirements

Doctoral degree (PhD or equivalent) or MS +3 years professional experience in a relevant scientific discipline with a significant computational component.
Minimum of 5 years professional experience working in and leading matrix teams across scientific and technical disciplines to deliver software projects integrating wet lab and dry lab capabilities in a continuously evolving landscape.
Minimum of 5 years of experience programming and software engineering using Python (preferred) and/or R.
Experience with modern software development tools / ways of working (e.g. object-oriented programming, git/GitHub, CI/CD).
Minimum of 3 years of experience using cloud technologies (e.g. GCP, AWS, Azure, Kubernetes) to build and deploy data workflow automation (e.g. Nextflow, Airflow), digital lab automation (e.g. orchestration, scheduling) and/or integrated (“lab-in-the-loop”) solutions.

Nice To Haves

Proven track record of designing, developing and deploying complex scientific software solutions with clear impact.
Strong leadership skills demonstrated by leading projects, influencing decisions and communicating with multidisciplinary teams and senior stakeholders in a cross-functional setting.
Demonstrated learning agility (e.g. working across modality or domains) and scientific curiosity while maintaining focus on driving impact in the face of ambiguity.
Experience mentoring or line-managing early-career scientists with a demonstrated focus on driving performance, skills, capability and career development to grow and improve the team.

Responsibilities

Collaboratively and iteratively develop and deploy LIAL Systems by partnering with scientists and technical teams across R&D and Tech to (re)design experimental processes for LIAL enablement and implement as integrated systems.
Translate scientific requirements into system designs and enabling platform capabilities and conversely articulate platform capabilities as system enablers of improvements to scientific processes.
Foster inclusive high-quality scientific and technical collaboration by working within and leading cross-functional matrix teams which mentoring and supporting others.
Introduce and apply new methods, tools, and data sources to drive innovation and continuously improve the speed, quality, and predictability of autonomous science using AI and Agents.
Prepare clear presentations and reports for project teams and decision-making forums.