FSP Associate Manager, Safety Data and Systems - Pharmacovigilance

Job description

Work Schedule Standard (Mon-Fri) Environmental Conditions Office Job Description Senior Director, Safety Data and Systems, Global Patient Safety or designeeJob Description:Design, develop and validate AI/ML and NLP components that support safety operations - including MedDRA/WHODrug auto-coding, case triage, duplicate detection and narrative summarization - with clear human-in-the-loop checkpoints Contribute to model lifecycle management for safety-relevant AI/ML: versioning, monitoring, drift detection, retraining and documentation aligned with GxP / GAMP 5 and internal model governance Support the qualification of AI/ML solutions against evolving regulatory expectations (EMA reflection paper on AI, FDA AI/ML guidance, EU AI Act obligations for high-risk systems) in partnership with Quality, DT/BIS and GPS Signal Management Serve as the key technical resource for the configuration, maintenance, and administration of the Oracle Argus Safety system. Support day-to-day operation and troubleshooting of safety systems. Assist in system validation, testing, and deployment of safety systems updates. Generate, validate, and customize safety reports and analytics. Collaborate closely with the pharmacovigilance, clinical, and regulatory teams to ensure safety data management aligns with global regulatory standards (FDA, EMA, PMDA, ICH). Participate in change management processes to enhance safety system integrations. Contribute to audit readiness activities, including system inspections, validation reports, and compliance documentation. Collaborates with internal systems team, BIS/ DT and Safety vendor on issues related to Safety data Performs the generation and quality control of aggregate reports and line listings Initiates and contributes to the development of procedural documents including but not limited to Safety Management Plans, SOPs, work instructions, job aides, forms, or templates Collaborates and co-creates with applicable client functions (e.g. Medical Information, Data Management, Business Information Systems, Quantitative Science) in regards to pharmacovigilance technical aspects, setup and operation Keeps up-to-date on applicable regulatory and PV tech guidelines and shares within GPS and client as applicable. Participates in training related to safety data management Proactively reviews processes and tools and provides suggestions for improvement and better efficiencies Complete additional task and projects as assigned by line manager or delegate 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.