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Associate Manager, Safety Data and Systems – Pharmacovigilance
Thermo Fisher Scientific. Design, develop, and validate AI/ML and NLP components supporting safety operations, including auto-coding, case triage, duplicate detection, and narrative summarization .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in AI/ML and NLP component design and validation for safety operations, with a strong focus on regulatory compliance and safety database management. Proficient in Python and deep learning frameworks, with a solid understanding of pharmacovigilance and quality management processes.
Highest-signal resume keywords
AI/ML Model Lifecycle ManagementPython ProgrammingNLP TechniquesSafety Database Systems (ARGUS, ArisG)Regulatory Compliance in Pharmacovigilance
ATS Keywords
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Hard Skills
PythonScikit-learnPandasNumPyPyTorchTensorFlowNLPSQLExcelMLOps
Soft Skills
Team CollaborationEffective PrioritizationIndependent WorkCommunication
Tools & Technologies
Oracle Argus SafetyMLflowAzure MLDatabricksMicrosoft 365
Industry Keywords
PharmacovigilanceGxPGAMP 5Quality ManagementClinical Research
Tech Stack
Tools & technologiesAzureNumpyOraclePandasPythonPyTorchScikit-LearnSQLTensorflow
About the role
Key responsibilities & impact- Design, develop, and validate AI/ML and NLP components supporting safety operations, including auto-coding, case triage, duplicate detection, and narrative summarization
- Contribute to safety-relevant AI/ML model lifecycle management, including versioning, monitoring, drift detection, retraining, and documentation
- Support qualification of AI/ML solutions against regulatory expectations in partnership with Quality, DT/BIS, and GPS Signal Management
- Serve as the key technical resource for configuration, maintenance, and administration of the Oracle Argus Safety system
- Support day-to-day operation and troubleshooting of safety systems
- Assist with system validation, testing, and deployment of safety-system updates
- Generate, validate, and customize safety reports and analytics
- Collaborate with pharmacovigilance, clinical, regulatory, client, internal systems, BIS/DT, and vendor teams
- Participate in change management to enhance safety-system integrations
- Contribute to audit readiness, inspections, validation reports, and compliance documentation
- Generate and quality-control aggregate reports and line listings
- Develop procedural documents, including Safety Management Plans, SOPs, work instructions, job aids, forms, and templates
- Stay current on regulatory and pharmacovigilance technology guidelines and share updates
- Participate in safety data management training
- Review processes and tools and recommend efficiency improvements
- Lead GPS Safety Data Management and Safety System Maintenance deliverables
- Provide high-quality data outputs for Safety Signal Management, Risk Management, and Safety Evidence generation
- Complete additional tasks and projects assigned by the line manager or delegate
Requirements
What you’ll need- At least Bachelor’s degree (or country equivalent) in computer science, data science, computational linguistics, applied statistics/biostatistics, life sciences, information technology, or another relevant field
- At least 3 years of proven experience with safety database systems such as ARGUS or ArisG, including workflow management
- Relevant experience in IT, Safety, Clinical Research, or Pharmacovigilance
- Equivalent combination of education and experience or proven practical expertise may be considered
- Proficiency in Python, including scikit-learn, pandas, and NumPy
- Experience with at least one deep learning framework, such as PyTorch or TensorFlow
- Experience with NLP, including adverse-event, drug, and outcome extraction from unstructured text
- Experience with named entity recognition, relation extraction, and text classification
- Experience with transformer-based or large language models, including BERT-family, BioBERT, PubMedBERT, or modern LLMs
- Experience with supervised and unsupervised classification, clustering, and anomaly detection
- Experience with feature engineering and model evaluation, including precision/recall, ROC/AUC, and calibration
- Experience with model lifecycle management, versioning, monitoring, drift detection, and retraining pipelines using MLOps tooling such as MLflow, Azure ML, or Databricks
- Model explainability/interpretability experience with SHAP or LIME
- Understanding of GxP/GAMP 5 validation, AI/ML model governance, and emerging regulatory expectations
- Working understanding of safety database data models, including Argus/ArisG, and E2B(R3) structure
- Advanced Excel and working proficiency in SQL required
- Proficiency in Microsoft 365 and collaboration/documentation tools
- Solid understanding of quality management processes, metrics, and KPIs
- Good knowledge of pharmacovigilance regulatory requirements and guidance documents covering Europe, the US, and Japan
- Fluent written and spoken English required
- Ability to work independently, prioritize effectively, complete multiple complex deliverables within tight timelines, and function effectively in a team environment
Benefits
Comp & perks- Fully remote work arrangement
- Standard Monday–Friday work schedule
- Healthy and balanced working environment supported by Thermo Fisher Scientific