Application Guide

How to Apply for Bioinformatician (Spatial & Single-Cell)

at Deep Science Ventures

🏢 About Deep Science Ventures

Deep Science Ventures (DSV) creates ventures from scratch to solve global challenges, like restoring ecosystems and reversing climate change. This stealthCo, built within DSV, is pioneering a causal AI drug discovery platform using primary human single-cell data, with validation from top researchers at the Allen Institute—offering a rare chance to work at the intersection of cutting-edge AI and biology in a mission-driven, seed-stage environment.

About This Role

As a Bioinformatician, you'll design and build production-grade pipelines for spatial and single-cell proteomics, directly feeding into an AI system that generates therapeutic hypotheses. Your work ensures high-quality data flows into causal biological networks, making every pipeline decision critical to the success of combination therapy discovery in oncology.

💡 A Day in the Life

Your day might start with a stand-up with the Head of AI to discuss pipeline priorities, then you dive into coding a spatial deconvolution module, testing it on a new Visium dataset. After lunch, you document the pipeline and review a pull request for a colleague's integration method, then end the day by analyzing QC metrics to ensure data quality for the hypothesis generation system.

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Interview Prep

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🎯 Who Deep Science Ventures Is Looking For

  • Expert in single-cell and spatial omics pipeline development (e.g., Scanpy, Seurat, Squidpy, Giotto), with hands-on experience in spot deconvolution, spatial autocorrelation, and ADT normalization.
  • Strong Python and workflow management skills (e.g., Snakemake, Nextflow) with a focus on reproducibility and production-level code.
  • Deep understanding of statistical methods for multi-modal integration (e.g., protein-RNA joint embedding, spillover correction) and hierarchical cell type annotation.
  • Comfortable working in a fast-paced, seed-stage startup with a remote-first culture, and able to collaborate closely with AI/ML researchers.

📝 Tips for Applying to Deep Science Ventures

1

Highlight specific pipelines you've built for spatial or single-cell proteomics, including QC metrics and integration methods—mention tools like SpaGCN or MENDER if used.

2

Showcase any experience with causal inference or biological network construction, as the role feeds into an agentic hypothesis generation system.

3

Tailor your resume to emphasize production-grade code and reproducibility (e.g., Docker, CI/CD), not just research scripts.

4

Include a brief note on how you stay current with new spatial technologies (e.g., Visium HD, Xenium, MERFISH) and any contributions to open-source tools.

5

If you have domain knowledge in oncology, mention it—the company's initial focus is cancer indications.

✉️ What to Emphasize in Your Cover Letter

['Emphasize your ability to build robust, scalable pipelines that ensure data quality for downstream AI systems.', 'Express enthusiasm for the mission-driven approach of DSV and the potential impact on drug discovery.', 'Demonstrate understanding of the technical challenges in multi-modal integration and causal inference from single-cell data.', 'Mention your collaborative experience working with AI/ML teams to translate biological data into actionable insights.']

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🔍 Research Before Applying

To stand out, make sure you've researched:

  • → Read about Deep Science Ventures' venture creation model and their portfolio to understand the company's mission and approach.
  • → Familiarize yourself with the Allen Institute's work on causal biological networks, as the company's system is verified against their researchers.
  • → Explore recent papers on spatial transcriptomics analysis tools (e.g., Cell2location, SpaGCN) and single-cell proteomics (e.g., CITE-seq, SCoPE2).
  • → Look into the company's stealthCo page or any publicly available info about their multi-agent AI system for hypothesis generation.
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💬 Prepare for These Interview Topics

Based on this role, you may be asked about:

1 Walk through your design of a spatial transcriptomics pipeline: what tools would you use for spot deconvolution and spatial autocorrelation, and how would you validate results?
2 How do you handle batch effects when integrating single-cell and spatial data from different platforms?
3 Describe a time you had to optimize a bioinformatics pipeline for production—what trade-offs did you consider?
4 How would you extend hierarchical cell type annotation from scRNA-seq to spatial proteomics data?
5 Discuss a project where you used statistical methods to correct for spillover in multiplexed imaging data.
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⚠️ Common Mistakes to Avoid

  • Don't submit a generic cover letter—this role is highly specific to spatial omics and causal AI; generic applications will be overlooked.
  • Avoid focusing only on scRNA-seq experience without demonstrating knowledge of spatial or proteomics modalities.
  • Don't overlook the importance of production engineering; emphasizing research-only scripts without consideration for scalability or reproducibility is a red flag.

📅 Application Timeline

This position is open until filled. However, we recommend applying as soon as possible as roles at mission-driven organizations tend to fill quickly.

Typical hiring timeline:

1

Application Review

1-2 weeks

2

Initial Screening

Phone call or written assessment

3

Interviews

1-2 rounds, usually virtual

✓

Offer

Congratulations!

Ready to Apply?

Good luck with your application to Deep Science Ventures!

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