Senior Director, Discovery Applied AI/ML

We are seeking a dynamic scientific and technical leader to build and direct a new Discovery Applied AI/MLgroup - 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 theapplied AI/ML engineand thought partner for GSK's Discovery functions, working in close partnership withDiscovery Data Sciencesand 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 onAI/ML innovation, engineering, and productization(e.g., generative design tools, active learning loops, LIAL frameworks)

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

• Serve as astrategic AI/ML partnerto 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 strategyfor Discovery, aligned with the broader Data, Automation, and Predictive Sciences (DAPS) and Discovery Data Sciences roadmaps.

• Establish and clearly articulate a vision for aresearch- and service-oriented applied AI/ML organizationfocused on:

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

• Direct enablement of automated discovery paradigms, includingLab-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 amulti-year strategic roadmapfor 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 theprimary applied AI/ML partnerto discovery and RTech line leaders, embedding your team into portfolio projects across therapeutic areas and modalities.

• Work intight coordination with Discovery Data Sciencesto:

• 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).

• Leadproblem-framing and solution designfor 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 frameworksfor 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 aculture 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)

• Allocateprotected timeand 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 aretranslated 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

• Championethical, transparent, and compliantAI/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 withDiscovery Data Sciences, Discovery Engineering & Integration, Automation, Cheminformatics, Protein Design & Informatics, and R&D Digital & Tech to:

• Architect and deliverAI-augmented platformsfor design, analysis, and decision support

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

• Co-leadpriority 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 tracktechnical and business KPIsfor 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 atrusted thought partnerto 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 theexternal AI/ML landscape:

• Evaluate emerging tools, platforms, and models for applicability

• Recommend strategic collaborations or partnerships where they can accelerate impact

• Providetraining, 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 mentora high-performing global team of:

• Applied ML scientists

• ML/AI engineers

• AI product managers / technical program leads

• Foster acollaborative, inclusive, and mission-driven culturethat:

• 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 planningfor 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+ yearsof experience in the pharmaceutical, biotech, technology, or closely related industry, withat least 8 years in leadership rolesmanaging multi-disciplinary AI/ML or computational science teams.

• Demonstrated track record of:

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

• DeployingAI/ML solutions into production environments and achievingtangible impacton scientific or business outcomes.

• Experience working withmultiple 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 intoreal-world medicinesfor patients.

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

• A Strategic Architect

• Experience designing and implementingautomated 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 tobuild 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.