Data Analyst supporting supply chain risk management for federal agencies. Focusing on data analysis and mitigation strategies in a collaborative team environment.
About the role
- Drive digital, data analytics, and GenAI capabilities for capital projects at GSK. Collaborate with teams to enhance project delivery through technology and governance.
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.
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).
Job title
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Tech skills
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