At Parallel Bio, we are leveraging the human immune system to unlock safer, more effective drugs. We believe immunotherapies are the future of medicine, but their discovery is hindered by outdated models that fail to capture the complexity of the human immune system.

Our platform overcomes these challenges by combining best-in-class human immune organoids with massive scale and advanced computational methods, including AI and machine learning. This allows us to generate unprecedented, population-scale insights into human health and disease.

We can rapidly discover new drugs that we know will work in patients from the start and understand how they will perform across an entire population—something not possible with today's technology. This knowledge will allow us to engineer therapies that will work for as many people as possible, ensuring a safe and effective cure for everyone.

We are a fast-paced, venture-backed company at a pivotal moment of growth. Join us on our journey as we create new tools to push the boundaries of what is possible.

The Role

We're hiring a Computational Biologist to accelerate scientific insight from population-scale immune organoid data. You'll work at the intersection of immunology, statistics, and modeling, developing and stress-testing analytical approaches that turn complex, noisy biological measurements into decision-grade conclusions including models that learn reusable structure from large, heterogeneous datasets.

You will prototype methods and reference implementations; Software/Data Engineering will productionize and maintain core pipelines.

Responsibilities

• Modeling & Methods Development
  Invent and adapt modeling approaches for immune organoid data - donor-aware inference, hierarchical models, robustness to batch and assay drift, uncertainty quantification in order to answer biological questions that matter. Learn and evaluate shared representations across assays, donors, and perturbations to enable transfer to new experimental regimes.

• Immune Profiling & Multi-Omics Analysis
  Own analysis for immune phenotyping across flow cytometry, CyTOF, single-cell RNA-seq, CITE-seq, and multiplex functional readouts. Characterize response signatures, quantify donor-to-donor variability and batch effects, and identify what changes under perturbation. Integrate transcriptomic, proteomic, and functional data to support target identification and biomarker discovery.

• Experimental Design & Scientific Partnership
  Guide experiments with quantitative thinking: propose designs and follow-ups that disambiguate hypotheses and reduce uncertainty efficiently. Partner with scientists to translate biological questions into computational workflows. Establish statistical best practices for exploratory vs. confirmatory analysis, multiple testing, and sensitivity analyses.

• Data Quality & Infrastructure
  Own the integrity of analytical datasets. Develop data quality standards, validation checks, and consistent annotation practices across experiments, donors, and timepoints. Build prototypes that scale socially: reusable analyses, gold-standard reference outputs, and clear definitions so others can trust and extend your work. Partner with Software/Data Engineering to transition validated pipelines into production.

• Communication & Visualization
  Generate clear, publication-quality visualizations. Explain assumptions, limitations, and implications to scientists, engineers, and leadership. Document methods and contribute to reports, presentations, and manuscripts.

Must-Have:

• Strong quantitative foundation (statistics, applied math, CS, engineering, or equivalent) and a track record of solving ambiguous modeling and data analysis problems

• Experience analyzing high-dimensional biological data (single-cell, cytometry, proteomics, functional assays, or similar)

• Immunology knowledge through formal training or hands-on research - you understand what biological questions matter and what the data represents

• Proficiency in Python and/or R for rapid, readable prototyping, with version control as standard practice

• Ability to translate across disciplines and drive work to completion in a fast-moving environment

Nice-to-Have:

• Bayesian/hierarchical modeling, causal inference, mechanistic-statistical hybrids, or ML approaches for biological data

• Experience with organoid systems or primary human tissue models

• Experience partnering with engineers to productionize data products (data contracts, validation checks, reference datasets)

• Track record training and stress-testing models on large biological datasets, with attention to generalization, robustness to distribution shift, and reusable representations

• Familiarity with cloud/cluster computing for scaling analyses

Parallel Bio is an equal opportunity employer committed to fostering an inclusive and respectful workplace. We encourage applications from individuals of all backgrounds, regardless of age, gender, ethnicity, religion, disability, or sexual orientation.