Data Scientist

Until is a moonshot company building a “pause button” for biology. Our near‑term focus is organ‑scale reversible cryopreservation: preserving donated organs at subzero temperatures without ice formation, then rewarming them uniformly for transplant. By solving this time‑critical challenge, we’re laying the foundation for medical hibernation: pausing molecular motion to give patients a bridge to future cures.

To achieve our goal, we are assembling an interdisciplinary team to develop perfusion systems, cryoprotectant formulations, controlled‑rate freezers, and vitrification and rewarming protocols for human tissue. We are also building out our medical hibernation team to tackle the challenges of whole‑body cryopreservation, beginning with rodent models.

We envision a future where no transplantable organ is lost to logistics, and no terminal diagnosis is final because patients can safely wait for future medicine to arrive.

About

The Role

As a Data Scientist at Until, you’ll own the full lifecycle of experimental data from generation in the lab to processing, analysis, and visualization. You’ll build the infrastructure and pipelines that move data reliably from instruments to insight, designing reproducible workflows and tools that help the team explore results quickly and clearly. You’ll work closely with scientists and engineers, setting a high bar for data integrity and usability across the team.

About You

• Degree in Computer Science, Data Science, Applied Mathematics, Statistics, or in a scientific field coupled with substantial data science experience

• Demonstrated ability to write modular, maintainable, and reproducible code in Python

• Proficiency with the Python data science stack, including Polars or Pandas, Num Py, Sci Py, Matplotlib, and Plotly

• Strong foundation in probability, statistics, and data modeling

• Experience designing schemas and working with structured datasets, with proficiency in SQL for querying and analysis

• Ability to communicate complex findings clearly to interdisciplinary teams

• Ability to think from first principles and tackle complex, cross‑disciplinary problems with other scientists and engineers

Preferred Qualifications

• PhD, Master’s degree, or 3+ years of relevant professional experience

• Experience with machine learning or predictive modeling, or working with large datasets

• Experience or exposure to a scientific field such as biology, chemistry, or physics

While this represents our expected range based on market data, final compensation will be determined based on your specific qualifications and may be outside this range. Please keep in mind that the equity portion of the offer is not included in this estimate.

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