Computational Proteomics Scientist — Mass Spectrometry & Multi-omics

Summary

8 years in computational proteomics spanning DDA, DIA, phosphoproteomics, and thermal proteome profiling. Built the proteomics data science function at Novartis from scratch, growing it to a 4-person team. Expert in DIA-NN and Spectronaut workflows, statistical analysis in R/Perseus, and integration of proteomics with transcriptomics and metabolomics. Instrumental in target identification for 2 development compounds.

Experience

Associate Director, Proteomics Data Science — Novartis (2021–Present)

• Built Nextflow DIA proteomics pipeline (DIA-NN → Perseus → R) processing 600+ LC-MS/MS runs/month on Azure; fully automated from raw files to statistical report.
• Led thermal proteome profiling (TPP) computational analysis for 3 target deconvolution campaigns; confirmed MoA for 2 clinical-stage compounds.
• Developed multi-omics integration framework (proteomics + RNA-seq + metabolomics) using MOFA+; identified novel synthetic lethality interactions in AML.

Postdoctoral Fellow — Gygi Lab, Harvard Medical School (2018–2021)

• Developed TMTpro phosphoproteomics workflow achieving 40,000+ unique phosphosites per experiment; protocol adopted by 8 labs.
• Built tidyproteomics R package (CRAN, 1,200+ downloads/month) for reproducible MaxQuant/DIA-NN output analysis.

Selected Publications

• Reyes I. et al. “Proteome-wide thermal stability profiling reveals AML drug targets.” Cell Chemical Biology, 2023.
• Reyes I. et al. “tidyproteomics: a tidy framework for reproducible mass spectrometry data analysis.” J. Proteome Research, 2021.

Code Quality Notes

R packages pass R CMD check with zero notes and include snapshot tests (testthat 3e) for statistical outputs. Nextflow pipelines validated with nf-test using real DIA-NN output fixtures. Raw MS data paths, software versions, and parameter hashes written to every output report for full audit trail. Azure pipeline costs tracked per project code via resource tags.