Paris, Île-de-France, France
I lead McKinsey and QuantumBlack teams focusing on deep tech especially Scientific AI across R&D. With data scientists, data engineers, integrative consultants/scientific translators we deliver product builds such as target id, drug discovery, trial optimization platforms, and lead R&D transformations around Scientific AI. I have expertise across data modalities (small molecules, omics, imaging, RWD, RCT, large knowledge corpus) and AI (ML, DL, GenAI, Agents). French-US citizen, I am based in Paris and work with our global teams to serve large players and smaller startups/scaleups around the world, from America to Asia.
As a lead data scientist, my job was to shape our omics R&D strategy and to lead the omics team (+4 people) responsible for acquiring human big genomics and omics data (from, e.g., NIH, UK Biobank, Clinical trials), developing machine learning/computational biology pipelines (GWAS, WGS, WES, RNAseq, using R, Python, Bash, Docker/Singularity, Nextflow, HPC and Big Data on Amazon Web Services), analyzing those data and combining them with available public data (statistical genetics, genomics/omics databases) to yield new candidate therapies and understand their mechanisms of action. My team was involved in all steps of the drug development pipeline, from discovery to clinical trials. Our work required strong collaboration within our data science department and other departments (e.g., preclinical, IT, clinical, regulatory, quality, legal). For this role, I reported directly to the Chief Data Science Officer.