Seinor Clinical Specialist-RWD/RWE

Role & responsibilities

Senior Specialist, Analytics & Insights (Clinical RWD/RWE Solutions)

Job description

This is an individual-contributor role within the Content Strategy team in Norstella Content Operations, reporting to the Director, Analytics & Insights (Clinical RWD/RWE Solutions). It is a hands-on clinical-analytical role: where the Director sets RWD/RWE strategy and owns the direction, this role does the clinical build - turning clinical and real-world-data expertise into the cohort definitions, endpoint logic, business rules and buildable specifications that underpin content products and AI solutions. The role is UK-based and remote.

The Senior Specialist works to specifications and standards the Director owns, and in turn translates those into precise, implementable logic that technical RWD engineers and data scientists can build without ambiguity. Focus areas:

• Defensible RWE logic - author and maintain the clinical logic behind cohort definitions, endpoint-based business rules and study-design choices, ensuring outputs are clinically sound and withstand scientific and client scrutiny
• Clinical translation - convert clinical reality and RWD expertise into precise, buildable specifications that Data Science and Technology can implement without ambiguity
• Evidence quality - safeguard the clinical plausibility of RWE outputs, identifying where technically valid results are clinically unsound
• AI-readiness (applied) - define the clinical decision logic, constraints and validation scenarios that keep AI- and agent-consumed outputs clinically coherent

Responsibilities

Own the clinical logic and specification of real-world evidence

• Author cohort definitions, primary and secondary endpoint logic, and real-world study-design choices across therapeutic areas (indication-agnostic), working to the standards and governance the Director sets
• Translate clinical and RWD expertise into precise, buildable specifications and business rules that Data Science and Technology can implement without ambiguity
• Understand the relationship between clinical reality and its imperfect representation in healthcare data - recognising artefacts introduced by coding behaviour, reimbursement processes, site-specific practice and incomplete longitudinal capture, and determining where absence of an event in the data can and cannot reasonably be interpreted as clinical absence
• Identify when apparently valid analytical outputs are clinically implausible, and challenge logic that is technically executable but clinically unsound
• Apply observational and epidemiological judgement to solution design - critically assessing cohort construction, index dates, follow-up definitions and endpoint selection, and recognising material sources of selection bias, information bias, confounding, censoring, missingness and measurement error
• Establish clinically justified assumptions where RWD is incomplete or ambiguous, identify appropriate proxy measures, and specify edge cases and exception handling required for robust implementation
• Define and own the statistical methodology for the RWE work in this role's remit - selecting and justifying the analytical approach, denominators, comparison and adjustment strategy, censoring and time-at-risk handling, and the treatment of missingness - ensuring the distinction between descriptive, associational and causal interpretation is applied correctly, and leading peer review of the engineer's analyses against that method (the engineer implements the method; this role owns its selection and defensibility for its own project work)
• Escalate to the Director, and partner with specialist epidemiologists or statisticians, where advanced or novel methodology is required beyond the standard RWE toolkit, or where a directional evidence-bar decision is required

Build and maintain reusable clinical RWD intellectual property

• Build and extend libraries of clinical definitions, cohort frameworks, phenotype logic, treatment algorithms, line-of-therapy rules, endpoint definitions, patient-journey constructs, documented assumptions, edge cases and validation scenarios - owning the clinical definition of each concept (what it means and why), with the technical computation of that same concept owned by the engineer
• Document clinical logic, rationale, assumptions and known limitations - with versioning and governance - so reusable logic can evolve as clinical practice, available data and product requirements change, and is not reused outside contexts in which it remains valid
• Own the clinical-fit lens on prospective RWD source assessment - evaluating longitudinality, completeness, representativeness, granularity, linkage and clinical specificity across EHR, claims, lab, registry and other datasets to judge whether a source can clinically support the intended use case, and flagging to the Director and Product where data limitations make a capability clinically unreliable (the engineer assesses technical feasibility in parallel; the Director owns the commercial go decision)

Support AI- and agent-ready evidence

• Define the clinical knowledge, decision logic, constraints and validation requirements for agents interacting with RWD, ensuring AI-enabled outputs distinguish clearly between observed information, calculated outputs, clinical assumptions and inferred conclusions
• Develop validation scenarios that test whether agent outputs remain clinically coherent across representative and edge-case patients, and identify failure modes where automated interpretation of RWD could mislead
• Own the clinical release decision for AI/agent outputs - reviewing the results of the engineer's validation harnesses, deciding whether outputs are clinically safe to release against the evidence bar set by the Director, and re-validating on a defined cadence as data, usage and failure modes evolve rather than treating validation as a one-time gate
• Contribute clinical input to whitespace identification and RWE enhancements, working with Product and the Director to scope disease-area priorities and requirements

Collaboration and delivery

• Partner with technical RWD engineers and data scientists - providing clinical context so they understand the intent behind what they build, and jointly resolving edge cases where clinical logic meets its data implementation
• Own clinical-plausibility QC - reviewing implementation outputs against the approved specification and against clinical reality, confirming that results are clinically coherent rather than merely technically valid, and signing off the clinical soundness of outputs before release (the engineer owns the underlying technical and data-quality QC)
• Work cross-functionally with Product, Services, Technology and Data Science as a clinical point of contact, escalating trade-offs and strategic decisions to the Director
• Deliver to a regular cadence against priorities set with the Director, and mentor or support more junior team members

Qualifications

Essential

• 7+ years' hands-on experience in RWD study design and analysis, working directly with sources such as open/closed claims, lab, EMR/EHR, registries, healthcare coding systems such as ICD-10 and NDC, and drug/medication data, with working fluency in US real-world data sources and their respective strengths and limits
• Strong clinical expertise - the ability to understand disease and treatment pathways, interpret healthcare data within the context of real clinical practice, identify clinically implausible assumptions or outputs, and reason through ambiguity where clinical reality is only partially represented in the data - together with the demonstrated ability to translate that reasoning into cohort definitions, endpoint-based business rules and precise, buildable specifications
• Ownership of statistical methodology for RWD/RWE within own project work - hands-on experience selecting, justifying and defending the analytical approach for real-world studies, with sufficient fluency in SQL and Python and/or R or SAS to read analytical scripts and outputs, direct and quality-assure the build, and give substantive feedback on both method and implementation (this role owns the choice and defensibility of method for its own remit; execution at scale sits with the engineer)
• A relevant clinical qualification - MD or equivalent medical qualification highly desirable; a Bachelor's or Master's in Life Sciences, Pharmacy, Medical Sciences or equivalent considered where substantial clinical knowledge and applied clinical reasoning on real-world healthcare data are demonstrated
• Understanding of the evidence needs of biopharma stakeholders across functions such as Clinical Development, Medical Affairs, RWE, HEOR and Market Access
• Excellent proficiency in Microsoft Office, including SharePoint, Excel and PowerPoint, Monday.com, LucidChart, Jira, Confluence, GitHub
• Impeccable or native English verbal and written communication, with experience working in global teams

Desirable

• Exposure to outcomes research, HEOR, market access or other biopharma evidence-generation use cases, particularly where this has involved the application of real-world data
• Experience integrating RWD/RWE into AI solutions and agentic workflows, or building content to be AI- and agent-ready
• Experience supporting the development of data, analytics, content or software products in life sciences or healthcare
• Prior experience mentoring or coaching junior analysts