Senior Director, US Decision Science & AI Innovation

• Own the certified method portfolio - marketing mix and investment optimization, next-best-action and dynamic targeting, patient predictive triggering, and causal measurement - driving reuse across brands and eliminating duplicate bespoke builds.
• Own deployment of those methods through enterprise capabilities - decision engines and simulators, embedded field and omnichannel triggers, planning and measurement tools - and be accountable for adoption and sustained use.
• Operate as a player-coach: personally design, review and build, setting the technical bar through direct involvement rather than oversight alone.
• Set and enforce method standards - validation, versioning, documentation, reproducibility and explainability - and define what is certified enterprise method versus brand configuration.
• Lead the applied AI agenda across agentic AI, generative AI and synthetic data - including multi-agent orchestration, retrieval-augmented generation and evaluation pipelines - with a prototyping pipeline whose explicit criteria either graduate techniques into production or retire them.
• Ensure methods, capabilities and AI applications meet GSK Responsible AI, privacy and data-use requirements, including model risk assessment, human oversight of automated recommendations, and permitted-use compliance for patient-level data, with a model registry and ongoing monitoring in place before anything scales.
• Partner with data engineering and the Digital & Technology organization for the platform and engineering these capabilities run on, and with data management for certified inputs.
• Own build-versus-buy decisions, biased toward building quickly and efficiently in-house; buy where it accelerates delivery and the capability can be absorbed into the GSK ecosystem. Hold vendors to internal standards.
• Act as method authority for brand teams and Insights & Analytics, translating demand into a prioritized roadmap sequenced against published capacity and communicating uncertainty and limitations to executive audiences.

• Bachelor's degree; Masters and PhD preferred.
• 12+ years of experience with hands on modeling.
• Experience deploying predictive analytics machine learning algorithms, deep learning algorithms and/or information retrieval at scale.
• End to end model exploration, development, deployment and management.
• Experience developing insights and storylines through translation of analysis and modeling
• Depth in commercial pharmaceutical methods - marketing mix modeling, next-best-action, patient-level predictive modeling or causal measurement - with accountability for results used in business decisions
• Production generative or agentic AI experience in commercial pharma, under enterprise governance - shipped and in use

• Build and deploy tools and methodologies to create solutions enabling analytics and measurement of sales and marketing activities (e.g., market-mix modeling, cross-channel attribution, omni-channel segmentation and customer targeting, patient pathways, omni-channel customer journey analysis and optimization etc.)
• End to end experience in retrieving data and developing an executive-level analysis through advanced analytics techniques
• Strong written and verbal communication skills
• Ability to work effectively across boundaries - both cultural and functional. This role requires partnership across business areas and with offshore teams, meaning a flexible communication style and astute cultural awareness are highly valued.
• Leadership capability is critical; You'll work in a matrix, inspiring change rather than commanding action. As a leader within the organization, we need an agile, confident decision maker - someone able to solve sophisticated problems with effective solutions.
• Experience setting agentic AI vision and governance across an organization rather than a single team
• Working command of current GenAI tooling - LLM APIs, prompting and context engineering, retrieval-augmented generation (vector stores, chunking, retrieval evaluation) and LLMOps evaluation pipelines
• Familiarity with multimodal data - structured claims alongside unstructured clinical notes and lab signals - and with US commercial data assets such as IQVIA, Komodo, Symphony and Veeva

#GSK-LI

Skills

AI Frameworks, Algorithm Optimization, Artificial Neural Networks (ANNS), Computer Programming, Deep Learning, Feature Engineering, Google Cloud Platform (GCP) for Machine Learning, Machine Learning Algorithms, Machine Learning Operations, Microsoft Azure Data and AI, ML Frameworks, Model Evaluation, Model Training, Model Validation, Performance Optimizations, Software Development Life Cycle (SDLC)

The US annual base salary for new hires in this position ranges from $186,750 to $311,250. The US salary ranges take into account a number of factors including work location within the US market, the candidate's skills, experience, education level and the market rate for the role. In addition, this position offers an annual bonus and eligibility to participate in our share based long term incentive program which is dependent on the level of the role. Available benefits include health care and other insurance benefits (for employee and family), retirement benefits, paid holidays, vacation, and paid caregiver/parental and medical leave. If salary ranges are not displayed in the job posting for a specific country, the relevant compensation will be discussed during the recruitment process. Please visit GSK US Benefits Summary to learn more about the comprehensive benefits program GSK offers US employees.