• Provides CDM leadership for one or more assigned projects or indications dependent on size and scale of the project • Takes global accountability and serves as the second line of contact at the project level • Lead studies including (but not limited to) a combination of healthy volunteer and patient populations, multi-site, complex protocol design, strong client management required or reduced timelines • Ability to organize and effectively prioritize workload and deliverables • Demonstrates leadership and operational expertise in the strategic planning and delivery of CDM deliverables at program and/or project level • Management and oversight of vendor contracts, resourcing and budget management and oversight of vendor performance for assigned programs, and projects • Communicates and negotiates effectively with all other Program level team members • Primary point of contact for Clinical Data Management (CDM) • Demonstrates a business understanding of the compound profile to identify and assist in successful application of consistent CDM processes and documentation across assigned programs, (i.e. ensuring consistency across data quality plans) • Provide oversight and expertise of external service providers or in-house teams to deliver quality data with compliance to study model procedures and standards; give guidance on company standards, processes, systems and expectations to external partners, internal partners and third-party vendors • Responsible for proactive risk management and issue resolution/escalation connected to Clinical Data Management improvement or technology • Develops an understanding of CDASH and SDTM or other recognized industry standards and impact to programming team to ensure consistency of program level standards • Specialist in TA specific data capture and standards, conducts lessons learned and disseminate across the organization as appropriate • May act as a team leader or mentor Clinical Data Management colleagues and any stakeholder with operational processes used in studies and projects • Demonstrates willingness to take on and lead any project level activity consistent with current or experience in support of study delivery

• Build and iterate on ML models — from data exploration and feature engineering through training, evaluation, and deployment • Implement and maintain components of production ML pipelines: data pre-processing, model serving, monitoring, and retraining workflows • Contribute to LLM-powered systems — building prompt chains, evaluation harnesses, RAG pipelines, or fine-tuning workflows • Analyse large multimodal datasets (text, image, video, structured metadata) to extract features and insights that feed downstream models • Write clean, tested, production-quality Python code — not just notebooks • Participate in code reviews, design discussions, and experiment retrospectives

• Explore and prepare datasets — cleaning, feature engineering, and exploratory analysis across structured and unstructured data (text, image, tabular) • Train and evaluate ML models under the guidance of senior scientists, learning how to move from a working prototype to a production-ready system • Write and maintain Python code that runs in production — scripts, pipeline components, and data processing jobs — with support through code review • Help build and test components of LLM-powered systems: prompt templates, evaluation scripts, data loaders, and retrieval pipelines • Run experiments systematically: track hypotheses, log results, and communicate findings clearly to the team • Learn and adopt software engineering best practices — Git workflows, testing, documentation, and CI/CD — as part of your daily work

• Design and run training pipelines - data curation, model selection, hyperparameter search, ablation studies - and be accountable for model quality on live traffic • Build and maintain production inference services (latency budgets, batching strategies, quantisation, monitoring) that serve WPP's global client base • Architect agentic AI systems: define tool schemas, orchestration logic, evaluation criteria, and failure modes for multi-step LLM workflows • Work across the stack when needed - write the data pipeline, train the model, build the evaluation harness, deploy the service, and debug it when metrics drift • Set technical direction for your workstream - write design docs, make build-vs-buy decisions, and defend your approach with evidence • Mentor and set the quality standards for junior scientists