Founding Computational Scientist, Proteomics
Hybrid (Cambridge, MA)Full-timePosted 2mo ago
About the role
Founding scientist responsible for building and owning the computational proteomics platform, including pipelines, analysis, data infrastructure, QC, and public data release for Deliverome Bio's surface proteomics atlas.
Responsibilities
- Build, containerize, and maintain pipelines for bottom-up LC-MS/MS analysis, DIA and DDA, that run reproducibly across hundreds to thousands of files
- Own the stack decisions: search and quantification software, workflow orchestration, compute and storage architecture, and cost
- Build the data model and provenance layer, covering sample and run metadata, parameter capture, versioned reprocessing, and the ability to answer which pipeline version produced a given number a year later
- Make reprocessing routine, so that improved methods are applied across the full corpus and not only to new data
- Design the quantification strategy for comparability across cell types and experiments, including normalization, batch structure, missing value handling, and protein inference and rollup
- Implement differential and ranking statistics with error control that holds up in review and in reuse by others
- Move toward absolute or ratio-anchored abundance estimates where the biology calls for it, and be explicit about the assumptions behind any copies-per-cell number
- Explore data completeness across cell and tissue types to understand where more data is needed, or when aspects of the atlas are sufficiently statistically empowered
- Build the atlas as a versioned, queryable resource with annotation and topology integration, confidence tiers, and a clear separation between what we measured and what we inferred
- Build QC reports, run-level dashboards, and result formats that let bench scientists check and interrogate their own data
- Partner with lab operations on the infrastructure upstream of analysis, including sample and cell line tracking, electronic records, metadata capture at the point of experiment, and file handoff conventions
- Own storage, backup, and data lifecycle for large instrument datasets, including what stays available, what gets archived, and what gets deposited
- Contribute to our general computational tooling as needs come up, and be someone colleagues can bring a data problem to
- Deposit raw and processed proteomics data to ProteomeXchange/PRIDE or MassIVE with metadata that makes reuse practical, and facilitate deposition of sequencing data to GEO and SRA
- Co-author methods and resource papers, and release pipelines and analysis code publicly
- As we scale and publish our data, integrate proteomic abundance with pooled screening readouts, transcriptomic references, and public surfaceome resources to produce interpretable target rankings and open data viewers
- Reanalyze published surfaceome and cell-surface capture datasets to benchmark our own results and to build on work the field has already done
Qualifications
- PhD in computational biology, computer science, proteomics, or a related field, or equivalent depth built in industry
- Deep hands-on experience analyzing large-scale bottom-up proteomics data, with fluency in at least one modern search and quantification stack (Spectronaut, DIA-NN, FragPipe/MSFragger, AlphaDIA, MaxQuant, Skyline)
- Experience running analyses at scale, cloud or local, using containerized and orchestrated workflows (Nextflow, WDL, Snakemake, or similar)
- Working command of quantitative proteomics statistics: normalization, batch effects, missing value structure in DIA versus DDA, protein inference, and FDR estimation
- Proficiency in Python and version control with git/GitHub, including releasing tagged, reproducible pipelines
Skills
Required:Pipelines and workflow orchestrationData modeling and provenanceQuantification strategy designStatistical analysisPython
Nice to have:Open data release
Benefits
- Competitive salary benchmarked to industry rates
- Full benefits including health, dental, and retirement
- A founding team role: your decisions will shape the platform and the organization
- Open science by design: your work will be published and used globally
- Unprecedented collaboration with world-class advisors across proteomics, functional genomics, targeted delivery, and AI
- A culture that values rigor, transparency, and speed, but doesn’t mistake busyness for productivity
Last updated Aug 7, 2026. Interested? Apply on the company site.
About Deliverome Project
Targeted delivery has a bottleneck.
The Deliverome Project is building an open atlas of the human surfaceome — abundance, specificity, internalization, and routing — to scale targeted delivery beyond the liver.
open atlas of the human surfaceomereceptorsdeliverytargeted deliverysurfaceomeADC targetsinternalizationrouting
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