Data Scientist II - Engineering R&D Support

A world leader in the field of in vitro diagnostics for over 55 years, bioMérieux provides diagnostic solutions which determine the source of disease and contamination to improve patient health and ensure consumer safety.In North America we have more than 5,000 team members across 11 sites or subsidiaries, including Salt Lake City-based BioFire Diagnostics and one subsidiary in Montreal, Canada. Come be a part of our team and our mission as a Data Scientist!bioMérieux - Salt Lake City is looking for a talented and engaged Data Scientist to play an integral role in the support of cutting-edge, life-saving medical devices. You will be joining a team that values collaboration, continuous improvement, and a healthy work life balance.The candidate will be knowledgeable in various aspects of data science, and responsible for performing complex mathematical and/or statistical analysis for projects to support the BioFire Engineering departments. The candidate must have strong analytic and programming skills. The candidate will be embedded in the Data Science department but will have close collaboration with the Software Development and Engineering departments at bioMérieux. Therefore, the candidate must have strong communication skills and a desire to work in a highly collaborative environment.Studies and Experience:A minimum of a B.S. in a quantitative discipline: Data Science, Statistics, Mathematics, Bioinformatics, Physics, Computer Science, Engineering, Computational Biology2+ years industry experience as a data scientist, mathematician, or statistician, or 0 years industry experience with a M.S. or PhD in a quantitative discipline.Programming proficiency in one or more of the following: R, Python, MATLAB, C++, C#, or SQLSkills and Qualifications:Good communication, writing, and presentation skillsExcellent analytical and problem-solving skillsPractical experience in any of the following: data visualization, data analytics platforms (e.g., Tableau), data pipelines, data cleaning and processing, probability, statistics, machine learning, image analysis, linear algebra, and optimization methods.