Capital Projects Digital & Data Analytics Lead

GSK

4d•Hybrid

About The Position

We manufacture and supply reliable, high-quality medicines and vaccines to meet patients’ needs and drive our performance. Our network of 37 medicines and vaccines manufacturing sites delivered 1.7 billion packs of medicines and 409 million vaccine doses in 2024 to help make a positive impact on the health of millions of people. Our supply chain is not just core to our operations; it’s vital to bringing our innovations to patients as quickly, efficiently and effectively as possible. Technology is transforming how we manufacture medicines and vaccines, enabling us to increase the speed, quality and scale of product supply. We need the very best minds and capability to help us on our journey to make more complex products, harnessing the power of smart manufacturing technologies including robotics, digital solutions and artificial intelligence to deliver for patients. As the Lead, you will be the single point of accountability for defining and delivering, in collaboration with Tech department, digital, data analytics and Generative AI (GenAI) capabilities that unlock value across capital project delivery (design, construction, commissioning, handover). You will translate enterprise strategy into pragmatic project-level solutions, drive safe adoption, mentor a delivery team, and ensure handover to operations

Requirements

Extensive years in capital project delivery or project controls, with proven experience leading digital/data/analytics initiatives in EPC, pharma, heavy industry or infrastructure.
Delivered end to end analytics/BI solutions and integrated engineering/project systems (BIM/digital twin, Primavera/MS Project, cost systems, EDMS, CMMS, IoT/OT).
Practical experience with GenAI/LLMs (prompting, fine tuning or model selection), MLOps (model registry, versioning, monitoring, retraining) and embedding models into workflows.
Managed multidisciplinary teams and vendors; negotiated contracts with data/IP protections.
Strong stakeholder engagement with PMO/Project Directors and governance bodies.
Knowledge of data governance, security and regulated environment compliance (GxP awareness desirable).

Nice To Haves

Pharma/regulated industry capital project experience.
Delivered GenAI use cases (document summarisation/extraction, conversational assistants, automated change request drafting).
Built digital twin use cases for sequencing, clash detection or commissioning simulation.
Familiarity with cloud (AWS/GCP/Azure), Databricks/Spark, BI tools (Power BI/Tableau) and MLOps frameworks (MLflow/Kubeflow).

Responsibilities

Own the programme’s digital, data and GenAI plan and goals; pick and prioritise the highest-value projects and report progress to sponsors.
Advise GCP leaders and governance groups on opportunities, risks and value; run cross-functional working groups to agree requirements, approve projects and resolve issues.
Set and enforce rules for who owns and manages data, how it’s organised and protected, and who can make decisions about project and GenAI data and outputs.
Turn business needs into clear, testable requirements for analytics and GenAI work; design how these tools will connect to BIM, scheduling, cost, documents and sensors, and check vendor designs meet our standards.
Lead projects from prototype to production, managing internal teams and vendors; negotiate supplier terms in collaboration with procurement that protect our data and IP.
Oversee GenAI models and manage datasets, model updates, deployment, monitoring and cost, and keep prompt libraries so outputs are reproducible.
Apply strictly GenAI governance and ethics: track data/source provenance, consent/IP rules, retention, acceptable use, human review points and audit trails; coordinate with Legal, InfoSec and regulators.
Deliver analytics and GenAI decision support (dashboards, assistants) integrated into project checkpoints (design reviews, QA/QC, commissioning, handover).
Drive change, training and adoption: build GenAI skills, role-based workflows and a centre of practice; provide validated prompt guides and measure adoption and value.
In collaboration with Tech, manage GenAI and data risks: prevent data leaks, reduce hallucinations, protect OT, address bias and model drift; use defensive controls like private models, encryption, and access controls and keep traceability for AI-influenced decisions.
Lead a small cross-functional delivery team, create reusable standards and workflows, capture lessons learned, and hand over all digital assets, models, prompt libraries and support arrangements to operations.