Profile:

• Clinical Data Scientist with a PhD in Biochemistry and over a decade of combined academic and industrial experience in clinical and biomedical research
• My interdisciplinary background - spanning molecular biology, cardiology, neurology, and data science - gives me both the medical knowledge to understand clinical data in context and the analytical and mathematical depth to manage, analyse, model, and extract meaningful insights from it
• Through involvement in clinical studies and regulatory environments, I have developed the ability to bridge medicine, statistics, and communication: translating complex data into clear, accurate, and regulatory-compliant reports that drive decisions in pharmaceutical, biotech, and medical device settings

Teaching:
2024 - today:
Place of Work: Paris
Role: Lecturer in Data Anonymization for Healthcare 
Customer: Aivancity School of Data Science & AI, Paris

2017 - 2024:
Place of Work:  Düsseldorf
Role: Lecturer in neurophysiology, sensory physiology, and ion channel pathologies 

Customer: HHU Düsseldorf

• Clinical trial design and statistical input across all phases: protocol, study design, sample size, and Statistical Analysis Plans (SAPs).
• Statistical programming: development of reusable functions and macros, program validation, and output generation.
• Production of Tables, Figures, and Listings (TFLs) for clinical study reports and regulatory submissions.
• CDISC standards: SDTM and ADaM data structure and mapping.
• EDC systems: MaganaMed, REDCap; eCRF design, implementation, and management.
• Clinical data monitoring, query management, and data cleaning.
• Creation and review of Data Management Plans (DMPs).
• Regulatory compliance: GCP (ICH E6(R2)), FDA (21 CFR Part 11), EMA, GDPR, MDR, GMP, HIPAA.
• Data anonymization and pseudonymization: k-anonymity, l-diversity, t-closeness, differential privacy, data masking, generalization, suppression, and noise addition; EMA Policy 0070, ISO 25237, GDPR/HIPAA requirements; statistical disclosure risk assessment (re-identification risk metrics using prosecutor/marketer/journalist models, information loss metrics, equivalence class analysis; sdcMicro).
• Domain expertise: cardiovascular biology, neurophysiology, oncology, and molecular mechanisms

• Biostatistics: hypothesis testing, power and sample-size calculation, survival analysis, mixed-effects and longitudinal models.
• Predictive and classification model development (regression, classification, machine learning).
• Epidemiological modeling: real-world data modeling, population estimates, risk assessment.
• Dimensionality reduction and clustering for high-dimensional biomedical data.
• Data visualization and interactive dashboards (R Shiny, Python).
• Synthetic data generation; utility and privacy evaluation of synthetic datasets.
• LLM/RAG implementation and optimization.
• Cloud services (AWS)

• Peer-reviewed publications in cardiology, neuroscience, and molecular biology (30+ publications).
• Systematic reviews, meta-analyses, scoping reviews, and consensus manuscripts.
• Regulatory medical writing: Clinical Investigation Plans (CIP), Clinical Investigation Reports (CIR), interim reports, periodic safety reports, registry reports, and clinical study synopses.
• Conference summaries, meeting reports, and congress proceedings.
• Critical appraisal of biomedical manuscripts: novelty, methodology, and literature coverage