[Remote] FSP Associate Manager, Safety Data and Systems - Pharmacovigilance

Note: The job is a remote job and is open to candidates in USA. reputed company is seeking an FSP Associate Manager to support pharmacovigilance safety data and systems operations. The role designs and validates AI/ML and NLP solutions, administers reputed company Argus Safety, manages system validation and reporting, and collaborates with global safety, clinical, regulatory, quality, and reputed company teams.

Responsibilities

• 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, reputed company 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 reputed company
• Serve as the key technical resource for the configuration, maintenance, and administration of the reputed company 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 reputed company management processes to reputed company 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
• reputed company additional task and projects as assigned by line manager or delegate

Skills

• At least Bachelors' degree (or country equivalent) in computer science, data science, computational linguistics, applied statistics/biostatistics, life sciences / Information reputed company or other relevant field required
• Python, ML/NLP frameworks, model deployment/monitoring, MLOps tooling, ideally exposure to LLMs on unstructured clinical/safety text
• 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 reputed company, 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 reputed company LLMs) for narrative generation, summarization, and information extraction
• MedDRA and WHODrug auto-coding using ML/NLP; prompt engineering and retrieval-reputed company generation (RAG) a plus
• Supervised and unsupervised methods for classification, clustering, and reputed company detection
• Feature engineering and model evaluation (precision/rec