Employer: U.S. Government Accountability Office
*All applicants are required to apply for the position on usajob.gov to be considered. The Partnership for Public Service has consistently named GAO as one of the Best Places to Work in the Federal Government. We have ranked in the top 5 Best Places to Work in the Federal Government every year since 2005 and our support of diversity has been top-ranked among mid-sized federal agencies since 2011. We’re an equal opportunity employer that strives to maintain a diverse workforce and an inclusive work environment. In addition, we focus on work/life balance, with flexible schedules and telework opportunities. Our 2019 work yielded a return of about $338 for every dollar invested in GAO.SummaryThis position is located in GAO’s Science, Technology Assessment, and Analytics (STAA) Team – Innovation Lab. STAA’s mission is to produce technology assessments, technical performance audits, science oversight audits, engineering sciences analyses, and advanced analytics and cybersecurity research to Congress and GAO overall.ResponsibilitiesLeads execution of projects throughout the innovation lifecycle from ideation through deployment to develop next generation advanced analytics capabilities in support of current and future audits, investigations, business operations, and other oversight challenges relevant to GAO’s mission. As necessary, the incumbent will work with experts outside GAO to determine leading practices for advanced analytics.Demonstrates expert level understanding of diverse data science techniques including: Machine Learning (ML, including supervised, unsupervised, and adversarial), natural language processing (NLP, including sentiment classification and topic modeling), deep learning, robotics process automation (RPA), dimension reduction, geospatial analyses, entity resolution, rules-based queries, graph-based network modeling, advanced visualizations, descriptive statistics, and other statistical/mathematical/analytical methods. The incumbent needs to demonstrate how ensembles of these techniques can be harmonized into cohesive, user-centric solutions.Demonstrates expert level experience in using common data science tools, including scripted languages such as R, Python, SQL, and Java Scripts; Integrated Development Environment (IDE) and analytics platforms such as RStudio, SageMaker, RapidMiner, SAS, and Domino Data Lab; open-source solutions such as Kibana, Kubernetes, and elastic search; commercial off-the-shelf tools such as Informatica and Neo4j; and hardware-based capabilities such as Graphics Processing Units (GPU).Demonstrates expert level understanding of cloud-based Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), and Software-as-a-Service (SaaS) capabilities. The incumbent will also direct implementations and configurations of tools and other computational resources within established information security boundaries.Provides expert advice, guidance and support to engagement teams across GAO on advanced analytics and related areas. Identifies key deficiencies in problem statements and/or proposed approaches or data access issues which are significant to the design, execution, and reporting out of advanced analytics pilots of the Innovation Lab.QualificationsIn addition to the education requirement, applicants must have 1 year (52 weeks) of specialized experience at the next lower band or level equivalent to the GS-13 in the Federal Service, or comparable private/public sector experience which has equipped the applicant with the skills and knowledge to successfully perform the duties of the position. Specialized experience for this position is defined as substantial, tangible experience applying data science competencies, including data imputation methodologies, dimension reduction techniques, complex entity resolution, natural language processing techniques, network/graph analyses, supervised and unsupervised machine learning methods, visualization, model/data governance, and user-centric design. The incumbent needs to demonstrate mastery in using common data science tools, including scripted languages (such as R, Python, SQL, and Java Scripts), Integrated Development Environment (IDE) and analytics platforms (such as RStudio, SageMaker, RapidMiner, SAS, and Domino Data Lab), open-source solutions (such as Kibana, Kubernetes, and elastic search), commercial off-the-shelf tools (such as Informatica and Neo4j) and hardware-based capabilities (such as Graphics Processing Units).