Über Zaher
Biostatisticien senior (PhD en biostatistique) avec plus de 10 ans d’expérience en recherche clinique, épidémiologie et data science appliquée aux données biomédicales et de santé publique. J’interviens en tant qu’expert statistique et data scientist sur des projets à fort impact scientifique, clinique et stratégique, en accompagnant des laboratoires pharmaceutiques, biotechs, medtech, CRO, startups en intelligence artificielle médicale ainsi que des équipes académiques et hospitalières dans la conception, l’analyse et la valorisation statistique de leurs projets data-driven.
Mes domaines d’intervention incluent :* Analyse statistique d’essais cliniques (Phases I–IV)
- Survival analysis avancée : Kaplan-Meier, Cox models, competing risks
- Exploitation de données en vie réelle (RWD/RWE)
- Validation statistique de modèles d’intelligence artificielle en santé
- Modélisation prédictive et machine learning explicable
- Développement de pipelines reproductibles (R, Python, SAS)
- Rédaction scientifique, protocoles, Statistical Analysis Plan (SAP), Clinical Study Report (CSR)
- Études médico-économiques et analyses d’impact budgétaire
J’interviens sur des projets stratégiques à fort impact clinique et réglementaire :
validation d’outils d’IA diagnostiques/prognostiques, identification de biomarqueurs, optimisation de designs d’étude, analyses de survie en oncologie, maladie rare.
Mon objectif : transformer des données complexes en résultats fiables, interprétables et actionnables pour la prise de décision médicale et business.
Französisch
Muttersprachlich oder zweisprachig
Englisch
Verhandlungssicher
Vor Ort möglich
Paris (bis zu 50 km)
Projekt- und Berufserfahrung
- Owkin FranceSenior Biostatistician | AI in Healthcare & Oncology | MedTech & BiotechBIOTECHNOLOGIEApril 2025 - Dezember 2025 (8 Monate)Paris, FrankreichWorked as a freelance senior biostatistician on multiple high-impact projects at Owkin, an AI-driven biotech company, contributing to clinical validation, analytical performance assessment, statistical modeling, and health-economic strategy for AI-based diagnostic and prognostic tools in oncology.Close collaboration with data science, medical, regulatory and product teams to ensure scientific robustness, regulatory readiness, and business relevance.
- Clinical Statistics & Survival Analysis: Led advanced survival analyses on oncology cohorts: Kaplan–Meier estimation, Cox proportional hazards models, Hazard Ratios (HR), 95% confidence intervals, Wald & log-rank tests, Clinical validation of AI-based prognostic risk scores (low-risk vs high-risk stratification), Analysis of long-term clinical endpoints (dRFI, IDFS at 5 and 10 years), Interpretation of results for clinical study reports (CSR) and scientific communication, Contribution to scientific manuscripts and publication-ready analyses.
- Analytical Validation & AI Model Performance: Statistical validation of AI models in digital pathology, Analytical performance studies including: Within-laboratory precision (WLP), Between-laboratory precision (BLP), Method comparison studies (e.g. H&E vs HES): Deming regression,Bias analysis,Bland–Altman plots, Definition of acceptance criteria, sample size estimation and validation protocols.
- Statistical Programming & Reproducibility:Development of robust statistical pipelines in R,Automated analyses, reusable functions, and standardized reporting,Setup and maintenance of Git repositories to ensure:Code traceability,Version control,Full reproducibility, Clear documentation for internal knowledge transfer.
- Health Economics, Pricing & Reimbursement Modeling: Design of economic models for AI medical devices:Fee-for-Service vs Bundled Payment systems,Cost-effectiveness and budget impact analysis.
- Scientific peer reviewer – Journal of the American Statistical Association (JASA).Senior Statistical Expert & Peer ReviewerFORSCHUNGOktober 2025 - Heute (8 Monate)Paris, FrankreichResearch article proposing a novel graph-theoretic, distribution-free test of randomness based on random interval graphs. The method detects complex dependencies (nonlinear, heteroskedastic, chaotic) beyond standard correlation tests and shows strong performance in simulations and real data applications.
- Wiley – Cancer Medicine JournalScientific Peer Reviewer – Oncology (Cancer Medicine, Wiley)FORSCHUNGApril 2025 - Heute (1 Jahr und 2 Monate)Londres, Vereinigtes KönigreichProject: "Colorectal Peritoneal Metastasis Incidence and Survival in the United States: A SEER Retrospective Cohort Study".Role: Biostatistician / Epidemiology & Survival Analysis ConsultantDomain: Oncology, Real-World Evidence (RWE), Population-Based Studies
- Data Source: SEER (Surveillance, Epidemiology, and End Results – US National Cancer Institute)
Tools: R, Survival Analysis, Cox Models, Kaplan–Meier, Epidemiological Methods.Led the biostatistical and epidemiological analysis of a large population-based retrospective cohort study using the SEER database, aiming to evaluate:- The incidence of colorectal cancer with peritoneal metastasis
- Overall survival and survival determinants in affected patients
- Prognostic factors influencing outcomes in a real-world US population
This project provides real-world evidence to support clinical understanding and research in advanced colorectal cancer.Epidemiological & Statistical Analyses:- Construction and cleaning of a large retrospective cohort from SEER
- Estimation of incidence rates and temporal trends of peritoneal metastasis
- Descriptive epidemiology: Age, sex, tumor characteristics
- Disease stage and metastatic patterns
- Survival analyses: Kaplan–Meier survival curves Median survival,Long-term survival probabilities,Multivariable Cox proportional hazards modeling to identify: Independent prognostic factors, Risk-adjusted survival differences.
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Ausbildung und Abschlüsse
- PhD en BiostatisqueUniversité de Paris2008Risque d'émergence d'un pathologie dans une population
- Master en BiostatistiqueUniversité de Montpellier, INRA, ENSA2005Modèles et outils de la biostatistique
Zertifizierungen
- py4e101x: Programming for Everybody (Getting Started with Python)University of Michigan2025
- PY0220EN: Python for Data Science ProjectIBM2025