FiveThirtyEight is seeking an organized, process-savvy and detail-oriented Data Editor to empower some of our newsroom’s most ambitious work. This full-time role is an opportunity to apply your analytical, programming and creative skills to all stages of a story’s evolution, from conception to publication. In particular, the Data Editor will be responsible for reviewing the methodological and statistical choices made in our stories, checking the accuracy of statistical and empirical claims made across all areas of coverage (politics, sports, science, etc.), publishing datasets we’ve created and maintaining internal reference databases. This role will be focused primarily on quantitative editing (including of our predictive models), but the Data Editor will also have the opportunity to create original statistical analysis and pitch stories of their own.
This full-time role with benefits is a U.S.-based position, and remote work may be considered. The Data Editor will report to the Copy Chief. To apply, please submit a cover letter and resume through the Disney Careers portal.
- Function as the quantitative editor on daily and feature stories — i.e., evaluate the methodological choices underlying our reporters’ and freelancers’ data analysis and statistical modeling, verify that any code used in that analysis is correct, and audit the statistical and empirical claims being made.
- Collaborate with the Copy Desk to manage daily and weekly editorial workflow, ensuring that quantitative edits are completed in a timely manner.
- Use statistical programming languages, such as R, to create original analyses and models to tell stories.
- Publish datasets to our public-data repository and work with the Interactives Team to maintain and improve those pages as resources for the public and our own work.
- Collaborate with reporters and the Interactives Team to create nuanced, statistically literate data visualizations and interactive projects.
- Experience conducting original data analysis, creating statistical models and critically evaluating others’ work.
- A deep understanding of traditional statistics (particularly causal inference) and, at a minimum, a conversational understanding of machine-learning techniques.
- Fluency with spreadsheets and at least one statistical programming language (ideally R) plus an enthusiasm for learning new technologies as needed.
- Familiarity with survey methodologies and the uses of public-opinion polling.
- Demonstrated success collaborating with kindness and generosity on large, multi-stakeholder projects and completing complex work under deadline pressure.
- The ability to derive satisfaction from patiently and incrementally improving and documenting evergreen data resources.
- Journalism experience or a robust understanding of the editorial process. If your experience was working with databases in a newsroom (or similar environment) or as a copy editor or fact-checker, that’s even better, as having meticulous attention to detail is key.
- Experience reviewing and evaluating academic papers to identify and correct methodological flaws.
- A solid understanding of U.S. politics, particularly electoral politics, and/or U.S. sports and sports statistics.
- A robust understanding of statistical models, such as those used to forecast sports or election results.
- Experience collaborating with developers on large statistical programming projects.