FSP Associate Manager, Safety Data and Systems - Pharmacovigilance

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.