FSP Associate Manager, Safety Data and Systems - Pharmacovigilance

Purpose of the role:

• Apply AI/ML and NLP methods to safety data (not limited to auto-coding, signal management support, narrative summarization, case triage) within GxP-validated, explainable and regulator-defensible frameworks
• Lead deliverables for GPS Safety Data Management and Safety System Maintenance activities
• Provide high quality data outputs for Safety Signal Management, Risk Management and Safety Evidence generation
• Collaborate and co-create with client functions and applicable vendors as required for seamless GPS Safety Data and Systems operations

Education and Experience:

• 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.
• Python, ML/NLP frameworks, model deployment/monitoring, MLOps tooling, ideally exposure to LLMs on unstructured clinical/safety text.
• Familiarity with, or ability to rapidly acquire, GVP/21 CFR 314 concepts preferred
• 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

In some cases, an equivalency, consisting of a combination of appropriate education, training and/or directly related experience, will be considered sufficient for an individual to meet the requirements of the role

Knowledge, Skills, and Responsibilities:

• 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.
• MedDRA and WHODrug auto-coding using ML/NLP; prompt engineering and retrieval-augmented generation (RAG) a plus.
• 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.
• Understanding of GxP / GAMP 5 validation as applied to AI/ML systems, model governance, and emerging regulatory expectations (EMA reflection paper on AI, FDA guidance) — rare and worth flagging as preferred.
• Proficiency in Safety Database systems (e.g. Argus) and knowledge of other technical systems applicable to Safety /Pharmacovigilance (e.g. E2B gateway, safety signal detection tools and systems) is a plus.
• 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
• Ability to communicate effectively and collaborate successfully across functions and with vendors
• Fluent communication in written and spoken English required
• Ability to work independently with minimal oversight and prioritize effectively
• Ability to complete multiple complex deliverables within tight timelines
• Ability to function effectively in a team environment

Working Environment:
Thermo Fisher Scientific values the health and wellbeing of our employees. We support and encourage individuals to create a healthy and balanced environment where they can thrive.