At least Bachelors' degree (or country equivalent) in computer science, data science, computational linguistics, applied statistics/biostatistics, life sciences / Information technology or other relevant field required.
Working understanding of safety database data models (Argus/ArisG) and E2B(R3) structure.
Relevant experience in IT / Safety / Clinical Research / Pharmacovigilance overall with at least 3 years of proven experience with safety database systems (e.g. ARGUS or ArisG) including workflow management
Equivalent and adequate combination of education and experience or proven practical expertise in all of the required skills
Proficiency in Python for ML development, including scikit-learn, pandas, NumPy; experience with at least one deep learning framework (PyTorch or TensorFlow).
Natural language processing for extraction of adverse events, drugs, and outcomes from unstructured text - case narratives, medical literature, call transcripts, and spontaneous reports.
Named Entity Recognition (NER), relation extraction, and text classification
Experience with transformer-based / large language models (BERT-family, clinical/biomedical models such as BioBERT or PubMedBERT, and modern LLMs) for narrative generation, summarization, and information extraction.
Supervised and unsupervised methods for classification, clustering, and anomaly detection.
Feature engineering and model evaluation (precision/recall trade-offs, ROC/AUC, calibration) with an understanding of why recall and sensitivity are weighted heavily in a safety context.
Model lifecycle management: versioning, monitoring, drift detection, retraining pipelines using standard MLOps tooling (e.g. MLflow, Azure ML, Databricks) in line with client DT/BIS standards
Model explainability / interpretability (SHAP, LIME) - essential where decisions must be defensible to health authorities.
Proficiency in electronic systems commonly used for Safety / PV, like for data visualization and analysis, dashboards
Solid understanding of the quality management processes, metrics and KPIs
Good knowledge of relevant pharmacovigilance regulatory requirements and guidance documents (including Europe, US, Japan)
Proficient in the Microsoft 365 stack (Excel, Word, PowerPoint, Teams, SharePoint, OneDrive) and in modern collaboration and documentation tooling
Advanced Excel required; working proficiency in SQL required for querying safety and operational datasets
Fluent communication in written and spoken English required
