Data Analyst, Centre of Excellence

Job Description: A. Clinical registry development and management Develop and maintain registry data dictionaries, metadata, coding specifications, case definitions, validation rules, standard operating procedures, data-entry guidance and change-control records. Design data-capture workflows and support appropriate extraction, integration or collection processes from clinical, operational and research systems.

Monitor registry participation, case ascertainment, completeness, timeliness, consistency, accuracy and follow-up status; identify root causes of gaps and support practical corrective actions with hospital teams. Prepare periodic registry reports that highlight outcomes, trends, case mix, variation, performance signals, data-quality issues and opportunities for clinical audit, research or service improvement.

Support preparation of registry data for approved clinical audit, research, quality improvement, benchmarking, presentations and peer-reviewed publication. B. Hospital-performance, operational-efficiency and VBHC analytic s Analyse complex hospital and service-line data to identify factors affecting operational performance, efficiency, capacity, patient flow, outcomes and value.

Develop analytical approaches to examine patient pathways and episode-of-care performance, including referral, admission, diagnostic, procedural, discharge and follow-up stages where data are available. Support hospital teams to identify unnecessary variation, bottlenecks, delays, avoid able utilisation, rework, complications, readmissions and other drivers of reduced efficiency or patient value.

Develop relevant measures for value-based healthcare, balancing patient outcomes and experience with resource utilisation and cost information where available, valid and appropriately governed. Produce actionable analyses to support improvement in length of stay, theatre and procedure-room use, bed management, outpatient flow, emergency-department processes, diagnostic turnaround, discharge planning, service access and other locally agreed priorities.

Apply appropriate stratification, risk adjustment, case-mix considerations and statistical methods when comparing hospitals, teams, patient populations or clinical pathways. Distinguish association from causation and communicate the limitations, assumptions, uncertainty and potential confounders of analyses clearly. Partner with quality-improvement and operational leaders to define baseline performance, monitor interventions and assess whether changes are associated with measurable improvement.

C. Advanced analytics, big data and AI-enabled methods Work competently with large, multi-source and potentially high-volume datasets, including structured and semi-structured health data, using efficient and reproducible approaches. Develop, test and maintain data pipelines for extraction, transformation, loading, linkage, de-identification or pseudonymisation, validation and analysis, in collaboration with authorised digital/IT and data-governance teams.

Use SQL and appropriate programming tools to clean, join, transform, analyse and document complex datasets. Apply suitable analytical methods, including descriptive analytics, inferential statistics, regression modelling, survival analysis, time-series or trend analysis, clustering, segmentation, forecasting and other methods appropriate to approved use cases and data maturity.

E. Dashboards, reporting and data storytelling Design, build and maintain clear, accurate and user-centred dashboards, scorecards,visualisations and reporting packs for hospital, discipline, network and executiveaudiences. Translate complex analytical results into concise, decision-ready narratives that state thequestion, key findings, limitations, implications and recommended next actions.

Produce routine and ad hoc reports in accordance with agreed reporting calendars, data-refresh schedules and stakeholder requirements. Job Requirement: Qualifications: A Master’s degree in a relevant field. Suitable disciplines include data science, health informatics, biostatistics, epidemiology, public health, healthcare analytics, computer science, mathematics, statistics, information systems, biomedical science, engineering or a closely related field.

Demonstrable formal or applied training in statistics, data analytics, data management,programming, health informatics, research methods or equivalent technical domains. A doctoral qualification, advanced professional certification, postgraduate researchexperience or specialised training in health data science, machine learning, clinicalinformatics or biostatistics will be an advantage.

Experience: At least three years of relevant professional, research or postgraduate analytical experience involving complex datasets, preferably in healthcare, clinical research, health insurance, public health, life sciences or a comparable data-intensive environment. Demonstrated experience in data cleaning, transformation, analysis and visualisationusing large or multi-source datasets.

Demonstrated experience writing and applying SQL queries and using at least onerecognised analytical programming language, preferably Python or R. Demonstrated experience using business-intelligence or dashboarding tools, preferably. Microsoft Power BI, Tableau, Qlik or an equivalent platform. Demonstrated ability to apply statistical methods appropriately and interpret resultsaccurately for non-technical stakeholders.

Experience with data-quality assurance, data documentation, reproducible analysis, datagovernance or secure handling of sensitive data.