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Deep Science VenturesDV

Bioinformatician (Spatial & Single-Cell)

Deep Science Ventures is a venture creator and fund, founded in 2016, that builds high-impact science companies to address global challenges in sectors like climate, agriculture, pharmaceuticals, and computation. They partner with entrepreneurial scientists and institutions, employing an 'outcome-first' approach to innovation and offering a unique Venture Science Doctorate program.

Deep Science Ventures

Employee count: 11-50

United Kingdom only

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StealthCo is a seed-stage techbio company, created within DSV, building a computational drug discovery platform that constructs causal biological networks from large-scale primary human single-cell omics data and structured published experimental literature. Our multi-agent AI system reasons over these networks to generate, simulate, and rank mechanistic hypotheses for combination therapies — with system accuracy verified against top-tier researchers at the Allen Institute. We will initially focus on oncology indications.

The Role (remote, timezone-restricted)

You will design and build production bioinformatics pipelines for new modalities—spatial transcriptomics, single-cell proteomics, and spatial proteomics—extending our existing scRNA-seq infrastructure. These pipelines feed directly into an agentic hypothesis generation system: the quality of what goes in determines the quality of every therapeutic hypothesis that comes out.

You’ll work closely with our Head of AI & Technology (Dr. Francesco Moramarco) and Head of Platform (Dr. Moustafa Khedr) to:

  • Build end-to-end pipelines (ingestion, QC, normalisation, integration, annotation, differential analysis)
  • Design modality-specific statistics: spot deconvolution, spatial autocorrelation, ADT normalisation, protein-RNA joint embedding, segmentation, spillover correction
  • Extend hierarchical cell type annotation across modalities
  • Codify best-practice workflows into reusable templates for agent execution
  • Sanity-check outputs to catch batch effects and artefacts before they propagate

Requirements

  • PhD in computational biology, bioinformatics, genomics, systems biology, or related quantitative field
  • 2–6 years experience in early-stage/high-growth startups
  • Pipeline-building experience with spatial transcriptomics (Visium, MERFISH, Xenium) from scratch
  • Experience with single-cell or spatial proteomics (CITE-seq, CyTOF, CODEX, IMC)
  • Strong Python engineering in the anndata ecosystem (scanpy/squidpy/muon)
  • Deep single-cell & spatial statistics knowledge (pseudobulk, multiple testing correction, mixed-effects models, compositional analysis)
  • Strong biology grounding; can distinguish biology vs confound; assess mechanistic plausibility
  • Timezone: at least 5 hours overlap with UK working hours (UTC−4 through UTC+4 preferred)

Strong desirables

  • Tumour biology / cancer immunology (TME, immune evasion, resistance)
  • Comfort working in an AI-mediated workflow and writing analysis plans executed by agents
  • Experience building pipelines/tools consumed by others; cloud compute (GCP preferred); R proficiency

Nice to have

  • Wet lab experience and familiarity with the 10x Genomics ecosystem

We know job descriptions like this can read as a wish list. If you don't tick every box, but believe you can build what we need - apply anyway! We care more about what you've built and how you think than whether your CV maps perfectly to every bullet point.

Benefits

Competitive compensation commensurate with experience and profile, plus equity participation. Flexible arrangement depending on location and preference (full-time employment or long-term consultancy). Small, technically intense team with high autonomy and ownership. Remote-first. Minimal management layers and direct impact on decisions.

About the job

Apply before

Posted on

Job type

Full Time

Experience level

Education

Postgraduate degree

Experience

2 years minimum

Location requirements

Hiring timezones

United Kingdom +/- 0 hours

About Deep Science Ventures

Learn more about Deep Science Ventures and their company culture.

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Deep Science Ventures (DSV) embarked on its journey in 2016, co-founded by Dominic Falcão and Mark Hammond. Falcão, with a background in philosophy, politics, and economics, had previously spearheaded Imperial College London's science startup program, gaining valuable experience with early-stage science ventures. Hammond, who had collaborated with Falcão at the Imperial Create Lab, brought a diverse academic background spanning neuroscience, AI, and neuropharmacology. Their shared vision was to create a new paradigm for applied science, moving beyond traditional tech transfer models. They aimed to establish an environment where companies could be intentionally formed to address specific, significant global challenges. This 'outcome-first' approach, starting with a societal or environmental problem and then working backward to identify technological and economic solutions, became a cornerstone of DSV's methodology.

From its inception, DSV set out to be more than just a venture capital firm; it positioned itself as a venture creator. The core idea was not to simply find existing intellectual property but to proactively combine scientific knowledge with entrepreneurial scientists to build high-impact ventures from the ground up. The company focuses on critical sectors such as agriculture, computation, climate, and pharmaceuticals. Over the years, DSV has refined its model, now operating on what they describe as version three of their company creation process. A significant development in their evolution has been the establishment of the Deep Science Ventures College and the innovative Venture Science Doctorate (VSD) program. This fully-funded, three-year PhD program is designed to train 'venture scientists,' empowering them to develop new core technologies and launch companies aimed at solving key problems in their respective fields. This initiative underscores DSV's commitment to nurturing talent and fostering a new generation of science entrepreneurs, aiming to significantly increase the number of science-based spin-outs. DSV's approach emphasizes collaboration, working with universities, intergovernmental organizations, and corporate partners to build a robust ecosystem for deep tech innovation.

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Deep Science Ventures

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