Common advice from experienced data scientists to job-seekers is to avoid job postings that describe a "data science unicorn": someone who has experience performing an unrealistically large array of technical and business-related job duties. Seeking a unicorn is viewed as a potential indicator that the company fails to understand their data science needs, and that new hires will not be poised for success due to lacking support and resources [Robinson & Nolis, 2019]. The R language, particularly when used with RStudio products, has evolved to enable production-level activities in the areas of data wrangling, reporting/dashboarding, database/software engineering, machine learning, and web application development. It is increasingly plausible that a data scientist will be able to efficiently perform a wide variety of job functions with experience only in a single language (R). Indeed, even entry level R users may tread into "unicorn" territory. Current standards for data scientist job descriptions and salaries do not accommodate this nuance, leaving both job-seekers and hiring managers unable to distinguish job requirements which should be read as warning signs from listings which are idyllic matches for the modern R unicorn. In this talk, we present data aggregated from several large compensation analytics companies which summarize current benchmarks for data science job descriptions and corresponding salary ranges. We then suggest job description language to target modern R users, considering both job duty compatibility and job post findability. These descriptions are presented with likely salary range pairings. Attention is given to deviations from traditional degree requirements, years of experience, and demands for multiple programming language literacy which may lack relevance for the R unicorn. Our overarching goal is to provide job description templates which encourage optimal matchmaking between R job seekers and organizations in need of their talents.
As academic faculty, I led cancer-focused research teams in the application / development of tools for applied machine learning, causal inference, and biostatistics. In the post-academic setting, I direct data science efforts for PCCTC, a CRO in the oncology clinical trials space. I am an enthusiastic proponent of the use of R for data science, and an advocate for productive remote working environments (I live and work in an RV).
