# Senior Data Scientist **Company**: IQVIA **Location**: Dublin **Work arrangement**: hybrid **Experience**: senior **Job type**: full-time **Category**: Data Science **Industry**: Healthcare **Apply**: https://iqvia.wd1.myworkdayjobs.com/en-US/IQVIA/job/Dublin-Ireland/Senior-Data-Scientist--Epidemiology---RWE-Analytics---FSP_R1573054?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply **Canonical**: https://yubhub.co/jobs/job_32fb44d8-d6b ## Description 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. **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. - 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 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 the most appropriate real-world data sources. - Extend analytical approaches beyond OMOP-based data when registries or other non-OMOP sources are better suited to the research question. **Rapid Analytics and Automation** - Identify opportunities to improve EpiRWE delivery through automation, reusable workflows, structured analytical specifications, and AI-enabled tools. - Define how reusable tools, agents, and human review checkpoints can support efficient and scalable analytical workflows. - Translate epidemiology and RWE needs into technical requirements, user stories, acceptance criteria, solution designs, and delivery priorities for engineering partners. - Partner with AI, data, and software engineers to develop solutions supporting concept-set development, cohort construction, feasibility analyses, cohort diagnostics, quality control, documentation, and analytical reporting. - Define validation criteria and quality standards for automated workflows and AI-enabled solutions. - Review and validate AI-generated or automated outputs for scientific accuracy, completeness, consistency, traceability, and alignment with study protocols. - Ensure that automation enhances analytical quality, efficiency, and scalability while maintaining appropriate scientific oversight. **Capability Building and Stakeholder Impact** - Help establish EpiRWE analytics as a scalable organizational capability rather than a collection of one-off studies. - Develop reusable templates, analytical standards, cohort libraries, study packages, quality-control frameworks, and documentation practices. - Measure capability value through improvements in delivery speed, analytical quality, reproducibility, reuse, stakeholder adoption, and decision impact. - Promote a culture of continuous improvement that challenges inefficient processes while maintaining scientific rigor and appropriate governance. - Contribute to the development of analytical best practices, reusable assets, and evidence-generation approaches across EpiRWE and cross-functional teams. **Required Qualifications** - Advanced degree in Epidemiology, Biostatistics, Health Services Research, Outcomes Research, Public Health, Biomedical Informatics, Data Science, or a related quantitative field. - Demonstrated expertise in epidemiologic study design, statistical analysis, causal inference, and the evaluation of bias and confounding. - Experience working with healthcare claims, EHR, registry, laboratory, linked, or other real-world data sources. - Hands-on analytical programming experience using R, SQL, and Python. - Experience developing and applying machine learning, AI, or advanced analytical methods to real-world data and evidence-generation use cases. - Experience with version control and reproducible analytical workflows. - Ability to translate scientific questions into protocols, analysis plans, analytical specifications, technical requirements, and delivery plans. - Strong understanding of data quality, analytical validation, quality control, traceability, and documentation. - Ability to communicate complex analytical findings clearly to both technical and non-technical stakeholders. **Why Join?** Those who join us become part of a recognized global leader still willing to challenge the status quo to improve patient care. **Benefits** No specific benefits mentioned. ## Skills ### Required - Epidemiology - Biostatistics - Health Services Research - Outcomes Research - Public Health - Biomedical Informatics - Data Science - R - SQL - Python - Machine Learning - AI - OMOP - OHDSI --- Source: [Apply at iqvia.wd1.myworkdayjobs.com](https://iqvia.wd1.myworkdayjobs.com/en-US/IQVIA/job/Dublin-Ireland/Senior-Data-Scientist--Epidemiology---RWE-Analytics---FSP_R1573054?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)