Senior Data Scientist
Dublin, Ireland
Hybrid/Site based
You will design, execute, and deliver rigorous, decision-relevant real-world evidence analyses while helping to advance a scalable, AI-enabled analytics capability. This is a hands-on role that combines epidemiological expertise, advanced analytics, and data science to deliver high-quality evidence and actionable insights.
The successful candidate will bring deep expertise in epidemiology, analytical programming, observational research, statistical methods, and real-world data. Working closely with the Head of Rapid Analytics and stakeholders across the EpiRWE therapy areas, including Virology, Oncology, and Inflammation, you will translate evidence needs into fit-for-purpose study designs, analytical requirements, executable plans, and high-quality results.
You will also identify opportunities to enhance the delivery of EpiRWE analytics through automation, reusable analytical assets, and agentic workflows. In partnership with dedicated AI and Data Engineers responsible for building and maintaining the underlying technical infrastructure, you will help define scientific and analytical requirements, guide implementation, validate outputs, and ensure technology-enabled workflows remain scientifically robust, reproducible, and valuable to stakeholders.
Key Responsibilities
EpiRWE Analytics Delivery
Partner with stakeholders to define evidence needs and translate them into clear research questions and analytical plans using OMOP and native datasets (e.g., EMR, claims, and molecular data).
Design, execute, interpret, and report observational and epidemiologic studies supporting assigned assets, indications, and therapeutic areas.
Conduct feasibility analyses, methodology development, statistical analysis planning, cohort definition, programming, quality control, results interpretation, and final reporting.
Ensure that analytical approaches are scientifically rigorous, fit for purpose, and aligned with the clinical, medical, regulatory, safety, or access decision being supported.
Provide guidance on study design, data-source selection, epidemiologic methods, potential biases, confounding, sensitivity analyses, and interpretation of findings.
Study Design and Analytical Methods
Apply advanced observational research methods to address complex research questions.
Evaluate assumptions, limitations, data completeness, measurement validity, confounding, selection bias, and generalizability.
Develop and review table shells, analytic data specifications, statistical analysis plans, derived variables, statistical models, and quality-control strategies.
Perform and oversee analyses using R, SQL, and Python.
Apply version control (GitHub) and reproducible research practices to analytical code, cohort definitions, specifications, documentation, and study outputs.
OMOP CDM, OHDSI, and Federated Analytics
Support the scientific and analytical use of the OMOP Common Data Model and the OHDSI ecosystem for rapid, standardized, and reproducible real-world evidence generation.
Translate study requirements into standardized concepts, concept sets, cohort definitions, temporal logic, inclusion and exclusion criteria, and analytical specifications.
Use and adapt OHDSI open-source tools and methods to support cohort development, cohort diagnostics, characterization, feasibility assessment, and multi-database analytics.
Develop reusable cohorts, phenotypes, concept sets, analytical modules, and study packages that can be applied across data sources, therapeutic areas, and research questions.
Support federated analytics by developing portable and well-documented study specifications that can be executed by approved data partners without transferring patient-level data.
Evaluate cross-database differences in coding, data capture, observation time, population characteristics, and analytical results.
Data-Source Evaluation and Feasibility
Conduct feasibility assessments to match research objectives with…