Senior Director, Discovery Applied AI/ML

We are seeking a dynamic scientific and technical leader to build and direct a new  Discovery Applied AI/ML group — a team dedicated to transforming how we discover medicines by embedding state-of-the-art AI/ML directly into our discovery workflows, platforms, and decision-making. 

We designed this role for someone with a desire to advance scientific knowledge and harness the revolution in AI/ML, automation, and predictive sciences to deliver measurable impacts across the drug discovery procress on the success and progression of our medicine discovery portfolio? 

This group will operate as the applied AI/ML engine and thought partner for GSK’s Discovery functions, working in close partnership with Discovery Data Sciences and other R&D  teams. In partnership with the Discovery Data Sciences which owns the core predictive modeling, analytics, and data science support across modalities, Discovery Applied AI/ML will: 

• Focus on AI/ML innovation, engineering, and productization (e.g., generative design tools, active learning loops, LIAL frameworks) 

• Rapidly translate emerging AI/ML methods and technologies into robust, deployed solutions 

• Serve as a strategic AI/ML partner to discovery line leaders, RTech teams, and platform owners 

You will lead a unified, cross-functional team of applied ML scientists, ML engineers, and AI product leaders who partner deeply with research units to solve their most critical challenges. 

This position is based 2–3 days per week at one of our R&D sites (e.g., Upper Providence, PA; Cambridge Tech Square, MA; Stevenage, UK; or Heidelberg, Germany). 

Key Responsibilities 

1. Strategic Vision & Organizational Architecture 

• Define and lead the applied AI/ML strategy for Discovery, aligned with the broader Data, Automation, and Predictive Sciences (DAPS) and Discovery Data Sciences roadmaps. 

• Establish and clearly articulate a vision for a research- and service-oriented applied AI/ML organization focused on: 

• Creation, evaluation, and deployment of state-of-the-art AI/ML techniques and platforms 

• Direct enablement of automated discovery paradigms, including Lab-in-an-Automated-Loop (LIAL) and other closed-loop experimentation systems 

• Design and implement an organizational model that integrates: 

• Applied ML research (novel architectures, generative models, active learning) 

• AI/ML engineering & platformization (scalable services, APIs, reusable components) 

• AI product management (use-case discovery, user-centric design, adoption) 

• Develop a multi-year strategic roadmap for how applied AI/ML will: 

• Increase the Probability of Technical and Regulatory Success (PTRS) 

• Shorten design–make–test–analyze cycles 

• Enhance decision quality across discovery programs 

2. Portfolio Impact & Scientific Partnership 

• Act as the primary applied AI/ML partner to discovery and RTech line leaders, embedding your team into portfolio projects across therapeutic areas and modalities. 

• Work in tight coordination with Discovery Data Sciences to: 

• Identify high-impact problems where AI/ML can drive step-change improvements 

• Decide when to advance from prototype to platform 

• Ensure clear delineation between core data science support (DDS) and advanced/applied AI/ML builds (DAI/ML). 

• Lead problem-framing and solution design for AI/ML use cases: 

• Generative design (molecules, proteins, biologics, modalities) 

• Active learning and optimization in high-throughput screening 

• AI-guided experiment planning and lab scheduling 

• Multi-modal integration for mechanism-of-action and target/context selection 

• Establish and maintain stage-gated, fail-fast frameworks for AI/ML projects: 

• Clear hypotheses 

• Success metrics tied to scientific or operational outcomes 

• Criteria for scale-up, sunset, or pivot 

• Communicate results and impact effectively to diverse audiences: 

• Scientific stakeholders (detailed methods, data, and models) 

• Platform & engineering teams (interfaces, requirements, performance) 

• Executives (value, risk, investment needed, portfolio impact) 

3. AI/ML Innovation, Engineering & Research Leadership 

• Drive a culture of pioneering applied AI/ML research, with emphasis on: 

• Generative models (e.g., diffusion models, VAEs, transformers) for molecular and protein design 

• Active learning, Bayesian optimization, reinforcement learning for closed-loop experimentation 

• Foundation models and large-scale representation learning across biological, chemical, and omics data 

• Multi-modal integration (e.g., sequence, structure, imaging, omics, real-world data) 

• Allocate protected time and resources for your team to: 

• Explore emerging methods 

• Run exploratory pilots with clear transition criteria 

• Contribute to publications, preprints, and community engagement (as appropriate) 

• Ensure that promising methods are translated into robust, maintainable solutions: 

• Collaborate with engineering and platform teams to build scalable APIs, services, and tools 

• Establish best practices for model lifecycle management (MLOps) in partnership with R&D Digital & Tech 

• Implement reproducible and compliant workflows for model development, validation, and monitoring 

• Champion ethical, transparent, and compliant AI/ML: 

• Ensure appropriate safeguards, interpretability, and documentation 

• Work with Risk & Compliance to align with regulatory and internal governance requirements 

4. Platform & Technology Build Leadership 

• Partner with Discovery Data Sciences, Discovery Engineering & Integration, Automation, Cheminformatics, Protein Design & Informatics, and R&D Digital & Tech to: 

• Architect and deliver AI-augmented platforms for design, analysis, and decision support 

• Enable LIAL and automated discovery frameworks, where AI/ML models actively inform experiment selection and optimization 

• Co-lead priority technology builds, ensuring: 

• AI/ML capabilities are designed as reusable components and services 

• Seamless integration with data platforms (e.g., Onyx, QEL) and lab automation systems 

• Alignment with enterprise standards for data, APIs, security, and compliance 

• Define and track technical and business KPIs for AI/ML systems: 

• Model performance and robustness 

• Usage and adoption metrics 

• Impact on cycle times, cost, and decision quality 

5. Thought Partnership & Internal Advocacy for AI/ML 

• Serve as a trusted thought partner to Discovery leadership on AI/ML: 

• Help shape the AI/ML aspects of discovery strategy 

• Advise on where to buy, build, or partner for AI/ML capabilities 

• In collaboration with the technology evaluation / innovation roles, continuously scan the external AI/ML landscape: 

• Evaluate emerging tools, platforms, and models for applicability 

• Recommend strategic collaborations or partnerships where they can accelerate impact 

• Provide training, education, and evangelism: 

• Help non-ML experts understand what AI/ML can and cannot do 

• Develop materials, seminars, and office hours for scientists and leaders 

6. Talent & Culture Development 

• Build, lead, and mentor a high-performing global team of: 

• Applied ML scientists 

• ML/AI engineers 

• AI product managers / technical program leads 

• Foster a collaborative, inclusive, and mission-driven culture that: 

• Encourages intellectual curiosity, experimentation, and continuous learning 

• Promotes psychological safety and healthy challenge 

• Rewards impact, rigor, and cross-functional partnership 

• Partner with HR and leadership on: 

• Hiring strategy and workforce planning for AI/ML roles 

• Career frameworks, competency models, and development pathways 

• Attract and retain top AI/ML talent by: 

• Providing compelling scientific challenges 

• Enabling visible impact on medicines for patients 

• Supporting opportunities for external engagement (conferences, publications, open-source where appropriate) 

Why You? (Qualifications & Experience) 

Basic Qualifications 

• Ph.D. in Computer Science, Machine Learning, Computational Biology, Computational Chemistry, Bioinformatics, Biophysics, or related quantitative discipline. 

• 12+ years of experience in the pharmaceutical, biotech, technology, or closely related industry, with at least 8 years in leadership roles managing multi-disciplinary AI/ML or computational science teams. 

• Demonstrated track record of: 

• Applying modern AI/ML methods (including deep learning and generative models) to complex biological, chemical, or healthcare problems 

• Deploying AI/ML solutions into production environments and achieving tangible impact on scientific or business outcomes. 

• Experience working with multiple data modalities (e.g., sequence, structure, images, omics, chemical structures, clinical/real-world data) and integrating them into AI/ML workflows. 

Preferred Qualifications & Skills 

• A Transformational Leader 

• Proven ability to build new organizations or significantly reshape existing ones. 

• Experience unifying disparate teams into a cohesive, high-performance culture. 

• An AI/ML Visionary 

• Deep understanding of modern machine learning, including generative models, representation learning, and active learning. 

• Clear perspective on how these methods can be practically applied to discovery R&D and automation. 

• An Influential Collaborator 

• Exceptional ability to build alliances and communicate a compelling vision to stakeholders across science, engineering, and executive leadership. 

• Skilled at influencing without authority in a complex, matrixed environment. 

• A Scientific & Technical Driver 

• Passion for science and rigorous engineering, with a relentless focus on translating computational innovation into real-world medicines for patients. 

• Experience co-creating technology with end users and platform teams to drive adoption. 

• A Strategic Architect 

• Experience designing and implementing automated research frameworks, experiment-in-the-loop systems, or MLOps architectures is a plus. 

• A Global Leader 

• Experience managing distributed teams across geographies and cultures. 

Why Join? 

This is more than a leadership role; it is a mandate to build the applied AI/ML backbone of discovery. You will be empowered with the resources, talent, and executive support to create a truly next-generation discovery engine that works hand-in-hand with Discovery Data Sciences and the broader R&D ecosystem. 

If you are a builder, a visionary, and a scientific leader driven to make a profound impact through applied AI/ML, we invite you to join us on this transformative journey.