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 analytics

• 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, preferablyMicrosoft 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.