Statistician

Ipsos•Washington, DC

4h•$110,000 - $120,000•Hybrid

About The Position

This role supports diverse USPA project teams in addressing client research needs by developing efficient and robust study designs and executing comprehensive, advanced inferential and predictive analyses. The statistician will assist senior researchers in generating complex weighting adjustments using machine learning and classical statistical procedures, implementing variance adjustments for survey inferences, blending samples, and monitoring data quality. Responsibilities include preparing and reviewing internal and external data, serving as a supporting author on client technical deliverables and proposals, contributing to technical literature reviews and solutioning sessions in statistics, data science, causal inference, synthetic data, and data integration. The role also involves working within interdisciplinary project teams to interpret and cross-validate findings, develop visualizations, and communicate results to clients and stakeholders, as well as interfacing with other technical teams to facilitate the deployment of high-performance computing resources.

Requirements

Masters or PhD in statistics, biostatistics, data science, computer science, or a relevant highly-quantitative social or behavioral science field.
Between 3-6 years of experience (depending on credential) in an applied research settings, preferably serving Federal or state statistical or policy agencies in the domains of population and / or behavioral research.
Formal training in one or more forms of longitudinal data analysis and their application to social and behavioral research (i.e., growth curve modeling, mixed effects models, panel methods, failure time / event history analysis, time series, etc.).
Experience with Census, health, public opinion, and demographic data.
Advanced skills in R and Python (must have experience using PyTorch); skill in SAS preferred.
Ability to convey complex technical topics to non-technical audiences, both verbally and in written form.
Proactive, and comfortable working within tight and shifting timelines.
Ability to support multiple projects simultaneously, while maintaining a strong client service orientation.
Unflagging attention to detail and commitment to the accuracy and completeness of information, quality of results, and robustness of findings.
Team player with strong interpersonal skills, and the potential to grow as a multidisciplinary quantitative researcher in a dynamic and entrepreneurial organization.
Flexible and comprehensive thinker with enthusiasm for learning and population research; solid foundation in the theoretical and practical aspects of applying statistics as well as data science tools and principles to custom, hypothesis-driven social and behavioral research.

Nice To Haves

Experience with Bayesian modeling and / or supporting the design and implementation of complex sampling and weighting plans a plus.

Responsibilities

Support diverse USPA project teams in addressing client research needs via developing efficient and robust study designs, and executing comprehensive, advanced inferential and predictive analyses.
Assist senior researchers in generating complex weighting adjustments using machine learning as well as classical statistical procedures, implementing variance adjustments for survey inferences, blending samples, and monitoring data quality.
Prepare internal and external data for analysis, and review data as they come in from the field.
Serve as supporting author on client technical deliverables, as well as narrative proposals.
Actively contribute to technical literature reviews and solutioning sessions in the domains of statistics, data science, causal inference, synthetic data, and data integration.
Work as part of interdisciplinary project teams to interpret and cross-validate study findings, develop visualizations and insights, and communicate results to clients and stakeholders.
Interface with other technical teams across Ipsos to facilitate effective deployment of high performance computing resources.