Data Scientist/Data Engineer at Nomic

At Nomic, our mission is to make biology easier to measure. We have developed nELISA, which stands as the world’s highest throughput proteomic platform. By integrating DNA nanotechnology, high-dimensional flow cytometry, lab automation, and machine learning, we are solving some of the most difficult challenges in protein profiling. Since spinning out of McGill University, we have partnered with many top-tier drug discovery groups, including six of the top ten global pharmaceutical companies. To date, we have profiled over 60 million proteins from more than 400,000 samples. Following a $42M Series B funding round, we are scaling our operations to meet significant demand, with our facility now capable of profiling over 2.5 million samples annually. We are a diverse team of engineers and scientists who value first-principles thinking and are dedicated to using the latest technological breakthroughs to drive our mission forward.

Key outcomes

• Design, build, and automate robust data pipelines and algorithms to process raw flow cytometry data from our multiplexed assays into quantitative protein measurements.
• Serve as a technical expert for the interpretation of nELISA experimental data, bridging the gap between biological fundamentals and data anomalies during R&D and lab operations.
• Develop internal-facing tools and data support features that empower our scientists to visualize and analyze datasets independently and efficiently.
• Collaborate closely with Data Engineering, Software Engineering, and Lab R&D teams to ensure our data infrastructure remains scalable and reliable.
• Troubleshoot complex data issues and establish the right abstractions for our growing data ecosystem.

Requirements

• A graduate degree in bioengineering or a related quantitative bioscience field, or equivalent industry experience, with a focus on biosensors or quantitative fluorescence data.
• At least 3 years of experience in analyzing bioscience data and developing advanced data processing algorithms.
• At least 2 years of software engineering experience, specifically in building toolsets for non-programming users within a collaborative environment.
• Proficiency in statistical methods, including Bayesian statistics, sampling techniques, and mixed models.
• Proven ability to work effectively with wet lab scientists, ideally within a fast-paced startup environment.
• Excellent communication skills and the ability to work independently as a problem solver.
• Fluency in English is required for collaboration with our US-based team, clients, and vendors.

Preferred qualifications

• Expertise in life science tools and laboratory methods, such as immunoassays, nucleic acid amplification, DNA nanoarchitecture, or signal processing.
• Direct experience optimizing surface chemistry, DNA-based circuits, fluorophores, or antibody-antigen interactions.

Compensation

We offer the opportunity to work in a fully remote capacity. While we do not have a specific salary range to disclose, we provide a collaborative and inclusive environment where your contributions directly impact the future of proteomics and disease detection.

How to apply

If you are passionate about building data pipelines for biology and want to drive innovation at the cutting edge of proteomics, we invite you to apply. Please submit your application to join us on our journey to redefine the understanding of biology.