Sr Data Scientist/Data Engineer

About us: Nomic was founded with a simple but ambitious goal: to make biology easier to measure. We’ve developed nELISA, the world’s highest throughput proteomic platform, by tackling some of the toughest challenges in protein profiling through a combination of DNA nanotechnology, high-dimensional flow cytometry, lab automation, and machine learning. Since spinning out of McGill University, we’ve partnered with dozens of top-tier drug discovery groups, including 6 of the top 10 pharma companies, and have profiled over 60 million proteins from more than 400,000 samples to date. Since closing a $42M Series B round, we recently scaled up the platform to meet rapidly growing demand. You can read more about this on our website here https://www.nomic.bio/news. Our state-of-the-art facility is capable of profiling over 2.5 million samples a year, generating 500 million protein assays. We’re a diverse team of engineers, scientists, and problem-solvers who thrive on breaking down difficult challenges using first principles thinking, and we leverage the latest scientific and technological breakthroughs to drive our mission forward. 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 senior IC on the team, you will sit at the intersection of our in-lab technology development efforts, our efforts to improve our data processing algorithms and infrastructure, and our work to develop internal tools for our scientists to more automatically visualize and analyze datasets themselves. As a jack of all trades when it comes to analyzing data and building tools for others to do the same, your day to day responsibilities will include: - Designing, building, iteratively improving, and fully automating the data pipelines and algorithms we use for processing raw flow cytometry data from our highly multiplexed bead-based assays into quantitative protein measurements. This will be done in close collaboration with your Data Engineering, Software Engineering, and Lab R&D teammates. - You will leverage your fundamental knowledge of biosensors, fluorescence data, and bioengineering R&D to act as an expert for the interpretation, and analysis of, nELISA experimental data when challenges arise in R&D and day-to-day Lab Operations, connecting the fundamentals of the science to the specific features or anomalies of the data. - You will also support R&D and Lab Operations teams through developing additional data support features and algorithms to support the growth of Nomic going forward. This will include any new data analysis pipelines to analyze nELISA data, including QC data from our daily manufacturing and profiling operations. - This role will involve substantial communication, teamwork, and attention to detail, especially when identifying and troubleshooting issues related to nELISA data and ensuring we build the right tools, and the right abstractions. - When tooling does not yet exist, you will leveraging your technical and bioscience domain expertise to develop new data analysis pipelines. What we’re looking for:  - Graduate Degree - or equivalent experience in industry - in bioengineering or a related quantitative field of study in the biosciences, with a focus on biosensors, quantitative fluorescence data, or similar. - 3+ years of experience specifically with analyzing bioscience data and developing improved data processing algorithms. - 2+ years software engineering/development experience - you must be comfortable standing up new toolsets for non-programming users, and coding in a collaborative environment together with experienced data and software engineers. - Statistical skills including bayesian statistics, sampling methods, mixed models, and experience applying other statistical concepts. - Strong past experience working collaboratively on data science problems with wet lab scientists, ideally in a startup or equivalent fast paced environment. - Nice to Have: Understanding of the fundamentals of life science tools, technologies and lab methods. In particular you would be an expert on multiple of: immunoassays, nucleic acid amplification, DNA nanoarchitecture and design, separation-based techniques for biological samples and compounds, biophysics / fluorescence, and signal processing. - Nice to Have: First hand experience optimizing (alone or in a team): surface chemistry (passivation, functionalization, regeneration), DNA-based circuits and DNA biosensor designs, fluorophores/fluorescence and FRET, antibody-antigen interactions and ligand binding, o