RWE Programmer
Description
We are currently seeking a RWE Programmer to join a sponsor-dedicated team supporting a global pharmaceutical partner. This is a fully home-based opportunity.
As a RWE Programmer, you will serve as a subject matter expert in Real World Data analytics and support the analysis of large healthcare databases to generate high-quality RWE for current and future compounds.
Working closely with RWE Scientists, Epidemiologists, Statisticians, and cross-functional stakeholders, you will contribute to study design, analytical strategy, database evaluation, programming, and interpretation of study results. This is a highly visible global role that combines advanced programming expertise with strong epidemiological and analytical knowledge.
Summary of Responsibilities
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Supports Statistical programming activities in Real World Evidence.
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Provides input on study design and analysis plans where necessary.
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Develops, validates, maintains, and documents analysis programs and other study documents.
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Provides input on interpretation of results and reviews publications
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Evaluates the quality of the database.
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Provides technical database expertise to stakeholders
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Ensures that statistical outputs are produced in an efficient manner.
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Reviews the study concept, protocol design, analysis plans and other study-related documents.
Qualifications (Minimum Required)
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Bachelor’s degree, preferably in mathematics, statistics, computing, life science, health science, or related subjects.
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Experience and/or education plus relevant work experience, equating to a Bachelor's degree.
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Fortrea may consider relevant and equivalent experience in lieu of educational requirements.
Experience
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Typically 3-5 years of experience in Real World Evidence, Epidemiology, Health Outcomes Research, or related environments.
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Experience working with large healthcare databases such a s Komodo, Marketscan, CPRD or MMIT)
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Experience in SAS or R programming within the pharmaceutical, biotechnology, CRO, healthcare analytics, or related industry.
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Python programming.
Skills & Competencies
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Strong understanding of Real World Evidence, epidemiology, and observational research methodologies.
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Knowledge of statistical and epidemiological analysis methods; experience with propensity score matching is a plus.
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Experience with Azure Databricks, PySpark, sparklyr, or other cloud-based analytics platforms is advantageous.
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Strong analytical, problem-solving, and data interpretation skills.
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Ability to communicate effectively with technical and non-technical stakeholders in a global environment.
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Strong organizational skills with the ability to manage multiple priorities and work independently.
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Learn more about our EEO & Accommodations request here .