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Member of Technical Staff, Intelligence - Functoional Genomics

Substrate Bio LondonEst. Est. GBP 85,000–120,000 / yearLead

Estimated range based on role, country and industry — not published by the company.

Key requirements

  • Python
  • Aws
  • Docker
LOCATION: King’s Cross, London · PATTERN: Hybrid, with regular time in the lab The opportunity We’re a stealth startup in AI and bio, building infrastructure for data generation. The team is small and elite, the problems are hard, and the foundations are being laid right now. You’d help write them from the first line, with real ownership and a direct line to the founders. This role owns the analysis of our functional genomics data. You would be the main computational partner to the scientists who run our perturbation screens, from how a screen is designed to which hits we call and how we show the evidence for them. About us AI for biology has a data problem. The data that matters most doesn’t exist yet, so we’re building the infrastructure to produce it, with quality and traceability built in from day one. We’re in stealth and heads-down on execution. We’ll share more about what we’re building once you’ve spoken with the team. The role You will join the intelligence team and own the analysis of our functional genomics data, working day to day with the scientists who design and run the screens. You will work with them from experimental design through to picking hits, and build the pipelines that let the analysis keep up as screening throughput grows. This is a hands-on role with real influence over how we generate and use our data. The data is multimodal. Single-cell and pooled CRISPR screens are read out by sequencing, by Cell Painting and other high-content imaging, and by proteomics, often on the same perturbations. Much of the job is making those readouts agree with each other, with statistics that hold up when a screen crosses several factors at once. You will work with the software team, who own how data is captured and stored. You build the analysis on top of that, and you own its quality. Your first 90 days FIRST 30 DAYS ◆ Learn how the scientists design and run the screens, and what each readout produces, from sequencing to imaging and proteomics. ◆ Review the analysis and QC that exist for CRISPR screens and Cell Painting, and set out what is missing. DAYS 30 TO 60 ◆ Own the analysis of the first single-cell and pooled CRISPR screens you work on: QC, guide assignment, differential expression and modelling of perturbation effects. ◆ Build the first image-based profiling pipeline for Cell Painting, from feature extraction and normalisation to phenotypic scoring. ◆ Advise on the design of upcoming screens, covering power, replication, library coverage, plate layout and batch effects. DAYS 60 TO 90 ◆ Integrate proteomic, transcriptomic and imaging readouts of the same perturbations, with statistics that hold up for multi-factor designs. ◆ Make the pipelines reproducible and versioned, with the software team, so they scale as throughput grows. ◆ Present results with their uncertainty and QC to the scientists and to leadership. Who you are You have a background in genomics, and you have analysed real screening or single-cell data and built things other people relied on. That might have been in a functional genomics or screening group in biotech or pharma, an academic lab, a core facility, or a computational group that worked closely with one. Nobody arrives with every part of this role. If you are strong in most of it and quick to learn the rest, we want to hear from you. You like working next to the bench. Much of the value is in the conversations with the scientists before a screen runs, and you can explain a result to someone who was not in the room. You write code other people can run, and you care how a result reads to the person receiving it. We do not hire people into boxes. This role will stretch past its description, sometimes into the lab and sometimes into conversations about how our data is used. Early hires here are expected to pick up what is in front of them. MUST HAVE ◆ A PhD, or a master’s with two to three years of relevant experience, in genomics, bioinformatic

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