FSP Associate Manager, Safety Data and Systems - Pharmacovigilance

Overview

Associate Manager role in Safety Data and Systems for Pharmacovigilance, applying AI/ML and NLP to safety data within GxP-validated frameworks while serving as the key technical resource for the Oracle Argus Safety system.

What you'll do

• 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
• Collaborate with internal systems team, BIS/DT and Safety vendor on issues related to safety data
• Perform the generation and quality control of aggregate reports and line listings
• Initiate and contribute to the development of procedural documents including Safety Management Plans, SOPs, work instructions, job aides, forms, or templates
• Collaborate and co-create with client functions (e.g. Medical Information, Data Management, Business Information Systems, Quantitative Science) on pharmacovigilance technical aspects, setup and operation
• Keep up-to-date on applicable regulatory and PV technical guidelines and share within GPS and client as applicable
• Participate in training related to safety data management
• Proactively review processes and tools and provide suggestions for improvement and efficiencies
• Complete additional tasks and projects as assigned by line manager or delegate

What you'll need

• At least a Bachelor's degree (or country equivalent) in computer science, data science, computational linguistics, applied statistics/biostatistics, life sciences, information technology or other relevant field, or an equivalent and adequate combination of education and experience or proven practical expertise in all required skills
• Relevant experience in IT / Safety / Clinical Research / Pharmacovigilance overall
• At least 3 years of proven experience with safety database systems (e.g. ARGUS or ArisG) including workflow management
• 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, 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
• 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)
• Working understanding of safety database data models (Argus/ArisG) and E2B(R3) structure
• Proficiency in electronic systems used for Safety / PV, such as data visualization, analysis and dashboards
• Solid understanding of 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 modern collaboration and documentation tooling
• Advanced Excel; working proficiency in SQL for querying safety and operational datasets
• Ability to communicate effectively and collaborate across functions and with vendors
• Fluent communication in written and spoken English
• Ability to work independently with minimal oversight, prioritize effectively, and complete multiple complex deliverables within tight timelines
• Ability to function effectively in a team environment

Nice to have

• Familiarity with, or ability to rapidly acquire, GVP/21 CFR 314 concepts
• Exposure to LLMs on unstructured clinical/safety text
• Prompt engineering and retrieval-augmented generation (RAG)
• Understanding of GxP / GAMP 5 validation as applied to AI/ML systems and emerging regulatory expectations (EMA reflection paper on AI, FDA guidance)
• Proficiency in other technical systems applicable to Safety / Pharmacovigilance (e.g. E2B gateway, safety signal detection tools and systems)

Details

• Location: Remote, North Carolina, USA
• Work schedule: Standard (Mon-Fri)
• Environmental conditions: Office