Associate Director RWD Scientist

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Associate Director, Real-World Data & Evidence Foundations

Data, Methods & Solutions · Patient-Focused Real-World Evidence (PRWE)

Role Overview

The Associate Director, Real-World Data & Evidence Foundations makes licensed real-world data fit for the studies and insights that depend on it. Claims, electronic health records are collected for care and billing, not for research. Each source brings its own challenges for new applications: gaps in enrollment, delayed resolution of claims, shifts between diagnosis coding versions. This person owns the path from a new data delivery to curated, documented, analysis-ready data a scientist can trust, and knows which of those problems matters for which question. They work on data platforms that IT and the enterprise platform teams own and run and bring the real-world data expertise those platforms need. Agentic AI coding tools are part of how they work every day.

Key Responsibilities

Real-World Data Curation

Assess each licensed claims, EHR and registry source for fitness for purpose before it is used: what it captures, and which questions it can and cannot answer.

Build and run the ingestion and curation pipelines that turn each delivery into analysis-ready data, handling enrollment and eligibility periods, claims lag and adjudication, and changes between coding-system versions.

Keep intact the details that change results, such as the decimal in an ICD-10 code, and document every transformation so a reviewer can trace any result back to the source files.

Map sources to common data models such as OMOP where reuse justifies it, and maintain the code lists, phenotype definitions and linkage rules that studies share.

Build standard cohorts and variables , with lookback and washout windows defined, so recurring questions do not start from raw data.

Check data quality and automated conformance, completeness and plausibility checks to inform each source’s fitness-for-purpose assessment.

Work with the platform teams on how each source is loaded, stored and refreshed, so a new data cut does not mean weeks of rework and routine cohort queries come back quickly.

Apply the terms of each data license and data use agreement, including access controls, small-cell suppression and output checks.

Agentic AI

Use agentic coding tools such as Claude Code routinely to write and test curation code, and maintain responsibility to review for quality and accuracy.

Work within company AI governance, and keep patient-level and licensed data out of any tool not approved for it.

Scientific & Strategic Leadership

Lead the design of standardized workflows which support complex non-interventional real-world studies and analytical strategies.

Strong understanding on how to frame research questions and define fit-for-purpose real-world evidence approaches.

Contribute standardized workflows for evidence strategies that support development, regulatory and broader product lifecycle decisions.

Anticipate methodological and data risks and propose practical solutions.

Decision Enablement

Translate repeatable analyses into clear, actionable insights for decision-making using standardized approaches.

Support portfolio-level decisions on evidence generation, data selection and analytical strategy.

Help ensure standardized evidence approaches are relevant to the questions being asked and the decisions they’re meant to support.

Cross-Functional Collaboration

Collaborate across clinical development, biostatistics, regulatory, medical, HEOR, data science and IT.

Work with IT and the enterprise data and platform teams, who own the shared infrastructure, so curation work fits company architecture and security standards.

Build trusted working relationships that make execution and alignment easier.

Minimum Qualifications

PhD, or a Master’s degree with equivalent experience, in epidemiology, biostatistics, statistics, data science, computer science or a related quantitative field.

8+ years in real-world data or real-world evidence, including hands-on work preparing claims, EHR or registry data for research use.

Working command of the coding systems these sources use, such as ICD, CPT, NDC, ATC and LOINC, and of mapping between them.

Knowledge of how the way real-world data is collected shapes confounding, missingness and misclassification, and what that means for curation choices.

Solid grounding in observational study design and causal inference.

Strong Python and SQL, written with software engineering habits such as Git and automated testing. Enough R to work alongside analysts who use it.

Regular hands-on use of agentic AI coding tools such as Claude Code on real projects, with the testing and review habits that keep the output trustworthy.

Clear communication of data choices and their consequences to scientists and decision makers.

Preferred Qualifications

OMOP CDM and OHDSI tools.

Experience with Israeli health data sources.

Linking or tokenizing patient records across real-world data sources.

Working on cloud data platforms with query performance in mind.

Computerized system validation (GxP) or other regulated data practice.

A publication record or open-source contributions in real-world data methods or tooling.

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