Biomarker Biostatistics Lead

argenx SE; Belgique; Sur place; EUR 70 000 - 90 000; Plein temps

Avantages offerts par ce poste

Competitive salary package

Extensive benefits

Résumé du poste

A biopharmaceutical company in Belgium is seeking a Senior Biostatistician to lead efforts in data reusability and Biomarker analysis. This role requires an advanced degree and 8+ years in pharma/biotech, with expertise in statistical methods, R, Python, and machine learning. The ideal candidate will champion reproducible research and contribute to regulatory interactions. A competitive salary package and extensive benefits are offered.

Qualifications

• 8+ years of experience in pharma/biotech.
• Expertise in statistical methods for Biomarker data.
• Clear communication and ability to translate complex findings.

Responsabilités

• Contribute to data reusability for metadata analysis.
• Champion reproducible research and compliance.
• Provide thought leadership in Biomarker analysis.

Connaissances

Biostatistics

Statistical writing

R & Bioconductor

Python

SAS

Machine learning

Data engineering

Formation

Advanced degree in Biostatistics or related field

Outils

Git

Databricks

Snowflake

Description du poste

Contribute to data reusability for metadata analysis and Reverse Translation. Champion reproducible research and traceability best practices; partner with Biomarker Data Management and Programming to ensure ALCOA++ compliance and conformance for regulatory interactions, where required. purpose lakehouse/warehouse patterns used widely in life sciences.Mentor, influence, and represent. Provide thought leadership in Biomarker analysis and data strategy, collaborate with statistics partners, and contribute to regulatory meetings as needed—consistent with senior biostatistics leadership responsibilities. Advanced degree in Biostatistics, Biomedical Science, Bioengineering, or related quantitative field; 8+ pharma/biotech experience spanning clinical development. Expert R & Bioconductor (package ecosystem fluency; writing robust, reproducible, tested code); Python for scientific computing and machine learning; SAS proficiency is valued. Expertise in surrogate markers, diagnostic, prognostic, pharmacodynamic and response predictive biomarker methods is a must; experience with regulatory strategies utilizing biomarkers a plusCommunication: clear statistical writing, and the ability to translate complex findings for crossDeep experience in statistical methods for Biomarker data, including feature engineering, dimensionality reduction, longitudinal modeling, survival analysis, multiplicity adjustment, and robust inference in highon experience in machine learning (classification, clustering, regularized models; ideally MLflowWorking experience with CDISC standards and submission metadata. Data & platform skills: Git, experience with Databricks/Snowflake; familiarity with data engineering patterns for analytics readiness, notion of CI/CD, containerization. Competitive salary package with extensive benefits.