Senior Data Scientist, Epidemiology & RWE Analytics - FSP

Description

Senior Data Scientist role in Dublin, Ireland, hybrid or site-based.

You will plan, run and deliver sound real-world evidence analyses that inform decisions, and help build a scalable, AI-enabled analytics function. The work is hands-on and draws on epidemiology, advanced analytics and data science.

You will work with the Head of Rapid Analytics and stakeholders across the EpiRWE therapy areas of Virology, Oncology and Inflammation, turning evidence needs into suitable study designs, analysis requirements, workable plans and reliable results.

You will also find ways to improve EpiRWE delivery through automation, reusable analytical assets and agentic workflows. Dedicated AI and data engineers build the technical infrastructure. You will set the scientific and analytical requirements, guide the build, check outputs and keep these workflows scientifically sound, reproducible and useful to stakeholders.

The company is a global provider of clinical research, commercial insights and healthcare intelligence to life sciences and healthcare organizations.

Responsibilities

• Work with stakeholders to define evidence needs and turn them into clear research questions and analysis plans, using OMOP and native datasets such as EMR, claims and molecular data.
• Design, run, interpret and report observational and epidemiologic studies for assigned assets, indications and therapeutic areas.
• Own the full study cycle: feasibility work, method development, statistical analysis planning, cohort definition, programming, quality control, interpretation and final reporting.
• Make sure analytical choices are scientifically rigorous and fit the clinical, medical, regulatory, safety or access decision they support.
• Advise on study design, data source selection, epidemiologic methods, potential bias, confounding, sensitivity analyses and how to read results.
• Apply advanced observational methods to complex questions, and assess assumptions, limitations, data completeness, measurement validity, confounding, selection bias and generalizability.
• Write and review table shells, analytic data specifications, statistical analysis plans, derived variables, models and quality control strategies.
• Run and supervise analyses in R, SQL and Python, and use GitHub and reproducible practices for code, cohort definitions, specifications, documentation and outputs.
• Apply OMOP CDM and OHDSI methods for standardized, reproducible real-world evidence: map requirements to standard concepts, concept sets, cohort definitions, temporal logic, inclusion and exclusion criteria and analytic specifications.
• Adapt OHDSI open-source tools for cohort development, cohort diagnostics, characterization, feasibility and multi-database analyses.
• Build reusable cohorts, phenotypes, concept sets, analytic modules and study packages that work across data sources, therapeutic areas and research questions.
• Write portable, well-documented study specifications that approved data partners can run in a federated way, without moving patient-level data.
• Assess cross-database differences in coding, data capture, observation time, population characteristics and results.
• Run feasibility assessments to match research questions with the right real-world sources, such as claims, EHRs, registries, laboratory data, linked datasets and patient-generated data.
• Use analytical approaches beyond OMOP-based data when registries or other non-OMOP sources fit the question better.
• Find opportunities to improve delivery through automation, reusable workflows, structured analytical specifications and AI-enabled tools.
• Define how reusable tools, agents and human review checkpoints can support efficient, scalable analytical workflows.
• Turn epidemiology and RWE needs into technical requirements, user stories, acceptance criteria, solution designs and delivery priorities for engineering partners.
• Work with AI, data and software engineers on solutions for concept-set development, cohort construction, feasibility analyses, cohort diagnostics, quality control, documentation and reporting.
• Set validation criteria and quality standards for automated workflows and AI-enabled solutions.
• Check AI-generated or automated outputs for scientific accuracy, completeness, consistency, traceability and alignment with study protocols.
• Keep automation improving analytical quality, efficiency and scalability while retaining appropriate scientific oversight.
• Help establish EpiRWE analytics as a scalable organizational capability rather than a set of one-off studies, including reusable templates, analytical standards, cohort libraries, study packages, quality control frameworks and documentation practices.
• Measure value through delivery speed, analytical quality, reproducibility, reuse, stakeholder adoption and decision impact.
• Drive continuous improvement that challenges inefficient processes while keeping scientific rigor and governance.
• Contribute to analytical best practices, reusable assets and evidence-generation approaches across EpiRWE and cross-functional teams.

Requirements

• An advanced degree in epidemiology, biostatistics, health services research, outcomes research, public health, biomedical informatics, data science or a related quantitative field.
• Proven expertise in epidemiologic study design, statistical analysis, causal inference and the evaluation of bias and confounding.
• Experience working with claims, EHR, registry, laboratory, linked or other real-world data.
• Hands-on analytical programming in 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 turn scientific questions into protocols, analysis plans, analytic specifications, technical requirements and delivery plans.
• Solid understanding of data quality, analytical validation, quality control, traceability and documentation.
• Ability to explain complex analytical findings clearly to both technical and non-technical stakeholders.

Conditions

Based in Dublin, Ireland, with a hybrid or site-based work model.

The company values diversity and equal opportunity, and all application information must be truthful and complete, as false statements can lead to disqualification or dismissal under applicable law.

Benefits

• Access to advanced technology, very large datasets, leading analytics tools and experienced colleagues.
• Career paths across geographies, capabilities, therapeutic areas and information technology areas, with room to change roles and grow.
• The chance to help clients advance healthcare and improve patient outcomes.