Role and Responsibilities: We are looking for someone who is comfortable executing existing software workflows, installing analysis packages, programming (Python or R) and command-line execution. Our ideal applicant is also expected to be well organized, an excellent communicator, and to work well in team environments. Familiarity with basic statistics and data visualization approaches will be critical. Job performance will involve a variety of activities including: Apply statistical learning approaches and generate clear visuals from biologically relevant datasets Apply and refine algorithms and software packages for internal use Work closely with scientists working in sequence analysis, basic statistical analysis, image analysis, chemistry, and biology to assist with analysis of large datasets Manage key datasets Collaborate with scientists to design experiments Apply and extend data analysis pipeline using statistically sound methodologies Generate actionable conclusions from imperfect data (e.g. missing values, batch effects, highly-skewed distribution, outliers, etc.) Qualifications and Education Requirements: PhD in Bioinformatics, Data Science, or related field 5+ years of experience in analyzing genomic, transcriptomic or proteomics data Expertise in data quality assessment and visualization Experience with programming in Python or R Ability to effectively organize, multitask and work in a fast-pace, deadline driven environment A willingness to follow direction as well as work independently Excellent written and oral communication skills A willingness to learn and grow within a transformative company Proven problem-solving skills and collaborative nature in fast-paced environment A willingness to wear multiple hats and be flexible with their work schedule Preferred Skills: Familiarity with programming in C++ or any other strongly-typed language Experience with using any of a number of integration, functional, and/or unit test frameworks to prove correctness of algorithms Ability to guide experimental design (design of experiments, power analysis, blocking, replication, and randomization)
