Data Analyst

ABOUT US Nomic was founded to make biology easier to measure—and to enable scientists to extend lives by making proteomics accessible, scalable, and routine. Our story began at McGill University, where our co-founders and early team developed breakthrough technology that would change the way proteins are measured. Today, Nomic is powered by our proprietary nELISA® platform—an end-to-end technology that delivers high-throughput, quantitative, and affordable protein data at unprecedented scale. With the launch of Omni 1000, we are making the $50 proteome a reality, breaking down barriers that have limited the field for decades. For the first time, large-scale, quantitative proteomics is accessible to any scientist, supporting everything from drug discovery and translational research to AI-enabled therapeutic development. Since our launch, we’ve partnered with leaders in pharma, biotech, and academia—including GlaxoSmithKline and the Broad Institute—and have profiled more than 500,000 samples across diverse applications. Our growth is fueled by over $60 million in investment from top-tier backers, including our recent $42 million Series B. We’re building a team of world-changers—scientists, engineers, and problem-solvers who believe that accessible proteomics can help shape the future of medicine. Nomic is headquartered in Montreal, Canada with a satellite research lab in Boston, Massachusetts. If you want your work to have real impact, we’d love to meet you. ABOUT THE ROLE: The Data team at Nomic is responsible for designing, building, operating and improving the data pipelines, data infrastructure, and data tools needed for analyzing nELISA data at scale. Our development roadmap includes building more robust data pipelines for decoding nELISA datasets, and developing improved internal-facing tools that will let our scientists execute faster in the lab by extracting insights from our nELISA profiling and manufacturing QC data on-demand. As a Data Analyst, you will be responsible for using Nomic's data analysis pipelines and tools to transform our raw readout data into quantified proteins in the context of our protein profiling business operations. You will be a critical link in the chain stretching from our customer's samples, through our profiling lab, and getting our customer's data back into their hands. As part of this role, you will be expected to troubleshoot any issues that may occur. This can involve identifying and implementing bug fixes within our pipeline or process, finding possible signs of irregularities in our process or samples, and investigation and root cause analysis on any QC failures. We also plan to have sufficient operational slack to permit production data analysts to join and assist the rest of the Data Team in general Data Engineering activities on a frequent basis. An experienced Production Data Analyst can leverage their position and high volume of data that passes by to build an intimate understanding of Nomic's pipelines and processes. They can also use their opportunities for troubleshooting and working with the rest of the Data Team to improve their fluency in Python. Both areas of growth open doors to further roles at Nomic. As a Data Analyst, you will play a critical, first-hand role in operating the data pipelines and data infrastructure for handling all things nELISA data. In particular: - You will primarily be responsible for using our existing software tools to analyze nELISA data. - Troubleshoot issues that occur, including deeper investigation and root cause analysis - Clearly communicate issues that occur, including any follow up investigation or root cause analysis. - Identify areas of improvement, and contribute to solutions. - Participate in process improvements and algorithm development activities - This role will involve substantial communication, teamwork, and attention to detail, especially when identifying and troubleshooting issues related to nELISA data. WHAT WE’RE LOOKING FOR: - Undergraduate Degree in any of Computer Science, Engineering, Bioengineering, Biology, or Applied Biosciences (or any closely related technical field). - Experience with Python and scientific Python stack (numpy, pandas, matplotlib, jupyter) - 1+ years of experience analyzing data and running data pipelines for bioscience data in practice, including in academic settings. - Familiarity with bayesian statistics, sampling methods, mixed models, and other statistical concepts, and ability to synthesize complex data into clear learnings. - Understanding of, and ideally first hand experience with analyzing data from, biotechnology tools and their associated methods, in particular in at least one of: sequencing, immunoassays, nucleic acid amplification, DNA nanoarchitecture and design, separation-based techniques for biological samples and compounds, biophysics / fluorescence / FRET, or signal processing (e.g. EEG, MRI). - Ability