Research Mathematical Statistician

We’re hiring a Research Mathematical Statistician at the U.S. Bureau of Labor Statistics (BLS) who will focus on developing novel statistical methodologies for the development and analysis of Federal statistical data. BLS publishes gold-standard data on labor market activity, working conditions, price changes, and productivity in the U.S. economy to support public and private decision making. These data are used by the U.S. Congress and state legislators to make policy decisions, by businesses to make decisions about new site locations and wages, and by millions of citizens to make critical career decisions. Together, these data provide a gauge on the performance of the U.S. economy. The work culture at BLS is one where our associates strongly believe in the importance of our vital mission to publish statistics of the highest quality for the American public. While our work culture is intensely focused on this mission, BLS also strongly believes in work-life balance. In this role, you will conduct research to devise novel statistical methodologies. You will carry out a research agenda and publish findings in peer-reviewed journals in areas of statistics or data science that advance the state of practice for the development or analysis of BLS data collected from survey and census instruments. You will present your findings at research conferences and play a leadership role to partner with other researchers in both government and academia to encourage methodological research targeting BLS data. You will serve as an expert methodologist who consults with BLS survey programs to develop and implement statistical methodologies that improve the quality of estimation and prediction for published statistics. Our researchers possess expertise in modeling, including nonparametric and machine learning approaches and Bayesian hierarchical probability modeling. Our models are used to conduct unbiased inference about an underlying population estimated on data acquired from a survey of that population and to develop accurate uncertainty quantification under dependence induced by the survey design used to collect the data. We are exploring modeling approaches to encode formal privacy protection into data products released to the public. Our data are typically time- and spatially-indexed and our statisticians express expertise in accounting for these sources of dependence. We also possess expertise in non-model-based approaches to provide variance estimators for published statistics estimated from survey data and to encode privacy protection into published tabular data statistics. We welcome applications from United States citizens. Tips for a successful application process: – Provide transcripts that document the degree requirements listed in the Qualifications section of the announcement. If you think the transcripts are not self-explanatory, you can add a section for “relevant coursework” to the education section of your resume highlighting the courses and credits that should count toward the requirement. Provide all relevant transcripts, not just your most recent one. – Document your relevant work experience through bullet points in your resume. Reflect the language used in the announcement where applicable (see Duties and Specialized Experience under Qualifications). Resumes are first reviewed by non-technical human resources staff that determine whether you are minimally qualified by ensuring your resume corresponds to the requirements listed in the announcement. – Your work experience bullet points should be tied to positions for which you list start and stop dates for employment as well as hour-per-week. This is how human resources staff determine whether your cumulative experience meets the work threshold (see Specialized Experience under Qualifications). – You do not have to limit your resume to one or two pages. You can take as much room as you need (within reason) to cover your qualifications for the position. – For more information on writing an effective Federal resume, read:

Federal resume writing guide .

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