AMPEL BioSolutions has a Research Analyst position available supporting R&D in an exciting, collaborative biotech start-up environment. AMPEL is a science-driven, “think-tank” organization looking for novel ways to bring precision medicine into the day-to-day lives of patients with systemic lupus erythematosus. Research projects have direct value to the mission of the company and often make a real difference in prioritizing drugs to be tested in pre-clinical mouse models or clinical trials of patients. Research Analysts will process gene expression data from lupus patients and healthy individuals using established statistical methods but with an emphasis on generating original tools tailored for company applications. They will also design, evaluate, and implement machine learning algorithms to carry out supervised prediction and patient classification tasks as well as unsupervised data exploration. Research Analysts will collaborate with systems immunologists to elucidate the “big picture” of pathogenic pathways operating in disease states in order to understand the immunological basis of autoimmune disease. Research Analysts must be able to synthesize results and formulate conclusions based on their data as well as the scientific literature and subsequently design experimental models for further inquiry. High-caliber work suitable for presentation at scientific meetings and/or publication in the peer-reviewed literature is expected. Individuals with a bachelor’s degree and two years of work experience and/or an advanced degree in biology, mathematics, statistics, biomedical engineering, computer science, or a related field are encouraged to apply. Candidates should have experience with analysis of transcriptomic data (microarray, RNA-Seq, single-cell RNA-Seq) and the ability to learn and apply machine learning concepts in R and/or Python. Successful applicants will possess excellent organizational skills, the ability to think critically, prowess in written and oral communication, and the initiative to work proactively with limited supervision. Outstanding candidates will be further distinguished by research experience.