Benni Vargas

About Benni Vargas

I am a computational biologist and data scientist with 9+ years of experience driving data-driven insights, with 7+ peer-reviewed publications across biotech and academia. I am currently at Vibe Bio, leading quality control and supporting product development for our AI-powered due diligence platform for biopharma clients, especially those targeting rare or overlooked diseases. Previously at Genentech (a Fortune 500 subsidiary), I collaborated closely with our Translational Medicine department to analyze patient clinical trial data to understand how our drug benefits patients, elucidate its pharmacodynamic effects and mechanism of action, drive early in development drug decision-making, identify biomarkers, and inform competitive public clinical messaging of our drugs in autoimmune and cardiovascular disease areas. To this end, my analysis toolkit includes:• Prompt engineering/AI literacy for automation and insight generation• Python• R• SQL• Multi-omics analyses: bulk RNA-seq, single-cell RNA-seq (scRNA-seq), proteomics• Statistical analysis and predictive modeling• Exploratory data analysis (EDA)• Data visualization (ggplot2, seaborn, matplotlib, Tableau)

Experience

1. Senior Scientist, Computational Biology & Translation

   Genentech

   Sep 2022 — Present · South San Francisco, CA, US

   Demonstrated our lead drug product benefitted patients regardless of baseline type I IFN gene signature and a wide spectrum of baseline serological biomarkers in the Phase II NOBILITY trial using logistic regression analysis of transcriptomic and clinical trial data in R (Open access link: Drove decision-making of one of our early-in-development product(s) through pharmacodynamic biomarker analysis in R of bulk RNA-seq data of clinical trial patients being treated with our late stage drug product• Leveraged internal and publicly available bulk and scRNA-seq data consisting of genes using Python and R to support scientific messaging around one of our molecule’s mechanism of action, targeting underlying pathogenic mechanisms.• Provided quality control and proteomic analysis for differentially expressed proteins of proteomic data (SomaLogic) in R for biomarker discovery of cardiovascular disease patients in collaboration with a teaching hospital.• Expanded capabilities of our AI framework analysis pipeline in Python in the following exploratory data analysis elements: univariate outlier detection, correlation, linearity testing, multicollinearity testing, hypothesis/statistical testing.

Education

• University of California, Los Angeles

  Bachelor of Science - BS, Microbiology, Immunology, and Molecular Genetics

  2013 — 2016

• Cerritos College

  Microbiology

  2011 — 2013

• University of Pittsburgh School of Computing and Information

  Graduate Certificate, Applied Data-Driven Methods

• University of Pittsburgh School of Medicine

  Doctor of Philosophy - PhD, Microbiology and Immunology