Our client, a leading healthcare organisation, is hiring a Data Scientist to join their team in Dublin on either a full-time or contract basis. The successful candidate will support clinical trial feasibility, study start-up, site selection and patient identification, developing predictive models to forecast trial performance and generate insights that help improve study planning, enrollment and overall outcomes.


Responsibilities

• Develop predictive models to support clinical trial feasibility, including country and site selection, enrollment forecasting, study start-up and patient identification.

• Design, build and assess regression, classification and ranking models to predict site performance, enrollment potential and trial outcomes.

• Apply effective model evaluation, validation and calibration techniques to ensure models are accurate, reliable and suitable for operational decision-making.

• Create and engineer features from diverse clinical and operational datasets, including site performance, investigator and KOL, epidemiological, patient population, competitive trial, CTMS and EDC data.

• Integrate and reconcile data across sites, investigators, protocols, studies and patient populations, resolving inconsistent identifiers across multiple systems and sources.

• Use causal inference techniques, including uplift modelling, counterfactual analysis and intervention effect estimation, to understand the factors influencing enrollment and study activation.

• Assess the potential impact of interventions such as adding or replacing sites, modifying patient eligibility criteria and adjusting site support strategies.

• Build scenario modelling and simulation capabilities to evaluate different site mixes, patient populations, enrollment assumptions and activation timelines.

• Support the development, deployment, monitoring and evaluation of models within a cloud-based MLOps environment.

• Communicate complex analytical outputs, including site rankings, enrollment forecasts and scenario analyses, as clear, actionable recommendations for sponsors and non-technical stakeholders.

Skillset

• Strong experience in data science, machine learning or advanced analytics, ideally within life sciences, pharmaceuticals, biotechnology, CROs or clinical research environments.

• Practical experience working with clinical trial feasibility, site selection, study start-up, patient identification or clinical development data is highly desirable.

• Strong proficiency in Python and commonly used data science and machine learning frameworks and libraries.

• Hands-on experience building and applying regression, classification and ranking models.

• Solid understanding of model evaluation, validation, calibration and performance measurement.

• Experience combining, transforming and engineering features from large, complex and varied datasets.

• Experience with entity resolution and master data linkage, particularly across sites, investigators, studies, protocols and related clinical data.

• Understanding of causal inference techniques, including uplift modelling, counterfactual analysis and treatment effect estimation.

• Familiarity with Azure Machine Learning, MLflow or similar cloud-based MLOps platforms.

• Experience working with lakehouse, Delta Lake or comparable modern data architectures.