Senior Data Quality Analyst

Longitudinal data quality control (QC) and bridge-sample strategy

• Design and implement a QC-sample-data strategy to support longitudinal comparability
• Define acceptance criteria and evaluate instrument performance, signal drift, and technical variation across batches and years
• Track QC metrics (e.g., PCA/control charts, run-order effects, feature variability, missingness) and issue fit-for-analysis QC certificates for each dataset cut.
• Investigate QC failures and coordinate corrective actions (reruns, recalibration, method updates) with platform teams.

Pre-processing, harmonization and batch correction

• Develop and oversee modality‑specific preprocessing pipelines (spectral preprocessing for FTIR/FRS; raw-to-feature workflows for MS proteomics and metabolomics).
• Implement QC-based drift and batch‑effect correction for both legacy and new data, with regular re‑estimation as methods evolve
• Standardize data schemas and feature identifiers to enable integrated longitudinal analysis.
• Maintain reproducible, version-controlled workflows (Git) with auditable SOPs, parameter registries and changelogs.

Collaboration, Knowledge Transfer & Process Improvement

• Collaborate with different divisions such as Laser, Biofacility, and Data Science teams to ensure QC‑certified data are formatted for downstream analysis and AI/ML models
• Mentor mid‑ and junior‑level team members on FTIR, FRS, and omics preprocessing, QC design, and batch correction methods
• Lead evaluation of new analytical platforms (e.g., FRS) and standardize analysis templates to promote reproducibility
• Contribute to scientific publications and internal reports documenting QC design and performance metrics