Will Macnair
Principal ML Scientist at Roche
Zurich, Zurich, Switzerland
Summary
👤
Senior🎓
Top SchoolWill Macnair is a Principal ML Scientist based in Zurich with eight years of focused experience applying statistical and machine learning methods to single-cell biology, particularly single-cell RNA-seq and cytometry. Trained as a mathematician with a PhD in Biostatistics from ETH Zurich and a background in mathematics from Cambridge, he bridges rigorous quantitative theory and practical biological insight. At Roche and previously at UZH and ETH, he has translated complex biological signals into actionable results using R, Python and MATLAB, and built reproducible analysis pipelines for neuroscience applications. He is practiced at explaining intricate mathematical concepts to non-mathematical stakeholders, enabling cross-disciplinary collaboration between wet lab and computational teams. Known among peers for an almost evangelistic enthusiasm for “all the single cell,” he combines deep domain expertise with a consultant’s clarity in problem framing and delivery.
8 years of coding experience
7 years of employment as a software developer
Mathematics, Mathematics at University of CambridgeDoctor of Philosophy - PhD, Biostatistics, Doctor of Philosophy - PhD, Biostatistics at ETH Zürich
Github Skills (91)
seq10
variational-autoencoder10
rna-seq10
autoencoder9
single-cell-analysis9
mixed-models9
hierarchical9
bioinformatics9
tsne9
sequential9
core-package8
vast8
density-estimation8
topological-data-analysis8
probabilistic8
Programming languages (10)
Github contributions (5)
wmacnair/SampleQC
May 2020 - Aug 2021
Robust, multivariate, multi-sample, multi-celltype QC for single cell RNA-seq data
Contributions:1 release, 67 commits, 1 PR in 1 year 3 months
bioinformaticsmultivariaterna-seqrna-seq-dataseq
wmacnair/psupertime
Apr 2019 - Jul 2020
psupertime is pseudotime ordering for single cell RNA-seq data with sequential labels
Contributions:22 commits, 50 pushes, 1 branch in 1 year 3 months
pseudotimeorderingbioinformaticsrna-seqrna-seq-data
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