FSP Associate Manager, Safety Data and Systems Pharmacovigilance

Work Schedule

Environmental Conditions

Job 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

• 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

• 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.