Job Description & How to Apply Below
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 technical role that turns approved clinical specifications and business rules into robust, scalable, production-grade RWD pipelines and analytical outputs. Clinical qualification is not required; deep technical fluency in real-world data engineering and analytics is.
The role is UK-based and remote.
Where the Director and the clinical specialists own what the evidence should be and why, this role owns how it is built
-developing the technical business logic that ope rationalises clinical intent against real-world data, and ingesting, transforming and standardising large claims, EHR, lab and registry datasets so cohort and endpoint definitions are implemented correctly, at scale, and are performant, reproducible, well-documented and AI- and agent-ready. Focus areas:
● Data engineering - build and maintain the pipelines that turn raw RWD into analysis-ready, standardised, quality-controlled datasets
● Technical business logic - develop the technical business logic that translates clinical intent into buildable data logic, working with the clinical specialists to confirm the clinical side and surfacing edge cases the specification does not yet resolve
● Data quality & performance - own profiling, QC and optimisation so outputs are reliable and scalable
● AI- and agent-readiness (technical) - structure and expose data so it is retrievable, semantically consistent and reliably consumable by AI solutions and agentic workflows Responsibilities Own the technical build of RWD pipelines and datasets
● Develop the technical business logic that ope rationalises clinical intent - designing how cohort, endpoint and business-rule definitions are actually computed against messy real-world data, including join logic, temporal windows, episode and line-of-therapy rollups, deduplication and code-set operationalisation
● Partner with the clinical specialists to pressure-test that logic - taking their clinical definitions as the authority on what the evidence should mean, confirming the clinical side of ambiguous cases, and surfacing edge cases, data artefacts and implementation choices the specification does not yet resolve so they can be decided jointly
● Ingest, transform and standardise claims, EHR, lab and registry data into analysis-ready form, implementing the agreed cohort, endpoint and business-rule logic correctly and at scale
● Build performant, reproducible and well-documented data pipelines and analytical code in SQL and Python (and/or R or SAS), applying software-engineering discipline - version control, testing, code review and modular, reusable design
● Own technical and data-quality QC - data profiling, completeness and null checks, referential integrity, distributional and record-count validation, and reproducibility of pipeline outputs - surfacing anomalies and data-quality issues to the clinical specialists and Director for clinical interpretation, while retaining ownership of the technical-QC layer itself
● Optimise storage, compute and query performance across cloud data-warehouse environments as datasets and use cases scale
● Implement statistical validation and analytical routines to specification, and give substantive technical feedback on approach, feasibility and performance
Enable AI- and agent-ready outputs
● Structure and expose content and RWE outputs so they are retrievable, semantically consistent and reliably consumable by AI solutions, agentic workflows and downstream products
● Implement the technical guardrails, constraints and validation checks specified for agents interacting with RWD, and build and run the test harnesses that exercise agent outputs across representative and edge-case data - surfacing the results to the clinical Senior Specialist for the clinical release decision and re-running them on the agreed re-validation cadence
● Partner with Data Science, AI and Technology teams to product ionise processes at scale and integrate RWD/RWE into AI-driven workflows…
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