Clinical Informatician (Senior Cancer RWD Analyst)

This is an exciting opportunity to take a key role in the AI Centre for Value-Based Healthcare, working with the Guy’s Cancer Real-World Evidence (RWE) team. The Clinical Informatician (Senior Cancer RWD Analyst) bridges RWD and RWE, with a focus on thoracic cancers. The post-holder will curate and develop regulatory-grade, deep cancer data assets, ensuring that outputs are clinically meaningful, aligned with research and commercial needs, and grounded in real-world cancer care delivery. Essential Criteria: Clinical background with understanding of oncology domain Expertise in clinical data and informatics, including source systems and clinical and oncology-specific vocabularies Expertise in SQL and Python (+/- R) to engineer and analyse datasets, validate cohort definitions, and generate insights and visualisations Understanding of cancer clinical research, pharma study / clinical trials, and regulatory requirements for RWD/RWE in oncology Excellent stakeholder engagement skills, with the ability to translate between engineering, clinical, and scientific audiences Excellent project management skills Desirable Criteria: Experience overseeing clinical validation of NLP-derived features extracted from unstructured medical records Track record of high-impact research output and/or commercial partnership delivery in RWE Experience building or working with solutions on cloud data platforms (e.g. Snowflake) Main duties of the job Curate and develop regulatory-grade, deep cancer data assets, focused initially on thoracic malignancies, by defining and validating cohort logic, clinical variables, and derived endpoints. Oversee clinical validation of features extracted from unstructured records (clinic letters, radiology, pathology, MDT) and feed back into pipeline improvement. Act as the clinical-informatics bridge between AI Centre data engineers and the Guy’s Cancer RWE team, translating between clinical, scientific, and engineering audiences. Take the analytical lead on highly complex cancer data, devising methods, setting data-quality KPIs, and making evidence-based recommendations to stakeholders. Engage and influence clinical, academic, and commercial partners, and contribute to research outputs and longer-term strategic partnerships. Lead and mentor within matrixed project teams, supporting capability-building and the effective use of project resources. AI Centre for Value-Based Healthcare The AI, Data \& Digital Innovation directorate is made up of data and technology experts - based in GSTT but working closely as a team with KCH and KCL. The team forms part of the Artificial Intelligence Centre for Value-Based Healthcare - a consortium of NHS, academic, and industry partners from across the UK. This consortium offers expert professional technical delivery across data engineering, data science \& AI development, and software engineering. Programmes include region-wide infrastructure delivery of cloud and federated platforms, multi-modal Real-World Data engineering, foundation model development, and development of different Language AI solutions. London / GSTT Snowflake Platform A secure data and research cloud platform that provides access to some of the broadest and deepest data in the NHS, including low latency patient-level data flows from primary care, linked to Acute Trust data. The platform also supports data science and deployment of advanced analytics and machine learning solutions, including Language AI for unstructured data extraction. Main Duties and Responsibilities Support the development of deep cancer data assets, with a primary focus on thoracic malignancies (NSCLC, SCLC, TET and mesothelioma), with possible subsequent expansion to other tumour types. Define and validate cohort logic, clinical and pathological variables, derived endpoints (e.g. real-world progression, response, treatment lines), and data quality rules, in collaboration with engineers and the Guy’s RWE thoracic team. Oversee clinical validation of NLP-derived features extracted from unstructured sources (clinic letters, radiology, pathology, MDT documentation), and feed back into model and pipeline improvement. Produce supporting documentation around data provenance, derivation logic, and quality, in line with regulatory requirements for RWD/RWE generation. Help to create standardised, tested, and fully provenanced cancer datasets to support both research and commercial delivery. Enable the thoracic RWE team to deliver international academic collaborations within the RWE international programme, such as IASLC. Develop analytic tools, datasets, and insights that support business planning, decision-making, and research prioritisation across the cancer RWD/RWE programme. Evaluate and analyse highly complex and sometimes contentious cancer data using tested and credible tools, applying robust problem-solving to identify root causes of data-quality issues and opportunities for improvement. Determine and track key performance indicators for the depth, coverage, and quality of cancer data assets, working collaboratively with clinical leads and operational teams. Manage analytical projects from inception to completion, working autonomously and demonstrating a leadership style consistent with the Trust values.