Senior Manager of Statistical Analysis and Quality Control

Hendall Inc•Rockville, MD

31d•$140,000 - $155,000

About The Position

Hendall is currently seeking a Senior Manager of Statistical Analysis and Quality Control to serve as the senior statistical, quality control, and behavioral health data lead on a project team supporting the U.S. Department of Health and Human Services (HHS). This is a senior management role with responsibility for technical leadership, independent review, and quality assurance of complex analyses and data products. The ideal candidate will have strong quantitative, data processing, and analytic skills and extensive experience applying population-based statistical methods to large, complex datasets, including administrative and survey data. The Senior Manager will lead rigorous statistical analyses and guide statisticians, data analysts, and other professionals; establish and enforce quality control standards, review analytic work produced by project staff, support AI-enabled analytics, and ensure that methods, results, data files, reports, and dissemination products are accurate, reproducible, consistent, timely, and publication-ready.

Requirements

Master's or PhD degree in statistics, biostatistics, epidemiology, public health, population health, data science, quantitative social science, mathematics, or a closely related field, with advanced training in statistical methods
At least 10 years of professional experience analyzing, processing, interpreting, reporting, and performing QC on large, complex datasets, including administrative and survey data, with progressively increasing responsibility
At least 5 years of experience analyzing and presenting behavioral health data, including substance use and/or mental health data, with demonstrated knowledge of population health, treatment services, or related administrative and survey data
Demonstrated experience applying population statistics and advanced univariate and multivariate methods, including descriptive and inferential statistics, hypothesis testing, regression, ANOVA/MANOVA, structural equation modeling, cluster analysis, trend analysis, and cross-sectional and longitudinal approaches
At least 8 years of experience using SAS, including SAS/STAT and related tools, to conduct statistical analyses, manage large datasets, develop reproducible code, and perform analytic quality control; experience with R, Python, and/or STATA to manipulate, analyze, visualize, and draw insights from large datasets; experience with AI-enabled data analytics and/or machine learning methods
Demonstrated senior-level technical leadership or management experience overseeing statisticians and data analysts, coordinating project tasks and priorities, independently reviewing analysts' work, validating statistical code and results, resolving QC findings, providing technical guidance, and determining whether analytic and reporting products are ready for release
Advanced data management, quality control, and reporting skills, including strong Excel capabilities, sound statistical judgment, and experience creating or reviewing data products, visualizations, dashboards, error reports, data audits, and technical documentation; excellent attention to detail
Exceptional analytical, problem-solving, technical writing, leadership, and verbal communication skills, including the ability to explain complex data and statistical methods in plain, actionable language, communicate quality concerns clearly, work independently, and collaborate effectively with multidisciplinary teams and stakeholders

Nice To Haves

Experience with federal or state administrative and survey data, including complex survey methods, weighting, variance estimation, population-level reporting, and formal quality assurance or quality control of statistical products
Experience serving as a senior analyst or QC lead for complex statistical projects, including responsibility for independent review, QC documentation, issue resolution, project coordination, and final technical recommendations on deliverable readiness
Experience applying AI-enabled analytics, machine learning, deep learning, or natural language processing to large datasets, including independent evaluation, validation, and quality control of model outputs
Five or more peer-reviewed journal articles, technical reports, or other substantial analytic publications
Knowledge of data de-identification procedures and best practices for protecting sensitive, confidential, or patient health information
Experience with data visualization and reporting tools such as Tableau and SSRS; experience leading or supporting QC reviews of federal statistical reports, data files, or public-facing dissemination products is highly desirable

Responsibilities

Provide senior-level technical leadership across statistical analysis, data processing, reporting, and quality control activities.
Establish QC expectations, direct and conduct independent validation of analyses, serve as a senior technical reviewer, and coordinate analytic work across a fast-paced, complex project.
Guide staff, track task status and priorities, resolve data quality and methodology issues, and ensure that deliverables meet established analytic, reporting, schedule, and QC standards.
Lead and conduct univariate, bivariate, and multivariate statistical analyses to describe populations, assess relationships, identify trends, and answer research and programmatic questions; provide senior statistical guidance on analytic design and interpretation.
Formulate and test hypotheses using appropriate inferential statistical methods, including regression-based approaches, analysis of variance, confidence intervals, model diagnostics, sensitivity analyses, and trend analysis, to answer research and programmatic questions; provide senior guidance on analytic design, interpretation, and statistical appropriateness.
Lead analyses of cross-sectional and longitudinal data, including repeated observations, linked records, and trend data, and review the interpretation of changes in populations and outcomes over time.
Analyze and oversee analyses of large administrative and survey datasets using population-based statistical methods to produce reliable estimates, rates, proportions, and other measures for reporting and decision-making.
Use statistical, machine learning, and AI-enabled data analytics techniques to identify patterns, improve analytic workflows, support automated anomaly detection, and solve complex data problems; independently validate AI- and machine-learning-assisted outputs for accuracy, reproducibility, and appropriate interpretation.
Lead quality control reviews of data files, statistical programs, calculations, tables, figures, and reports; independently reproduce key results, investigate discrepancies, verify analytic logic and assumptions, and work with stakeholders to resolve data quality and methodology issues before release.
Establish, document, implement, and continuously improve formal quality control procedures, review checklists, issue-resolution processes, and QC documentation for statistical analyses, data processing, and dissemination reports and datasets.
Lead quality control reviews of data files, statistical programs, calculations, tables, figures, visualizations, dashboards, reports, and technical documentation; independently reproduce key results, verify analytic logic and assumptions, investigate discrepancies, and determine whether products are ready for release.
Develop and independently review SAS programs for statistical analyses, quality checks, ad hoc data runs, and reproducible analytic workflows; review code prepared by others and use R, Python, STATA, or other tools as appropriate for data manipulation, analysis, visualization, and automation.
Oversee special data analyses, feasibility studies, data mining and exploration, and ad hoc data requests; prepare or review clear write-ups, tables, figures, summaries, and data products that translate complex findings into simple, actionable information for technical and nontechnical audiences.
Serve as a senior technical and QC reviewer for analytic data files and documentation, including restricted-use and public-use files, programming specifications, and SAS/ASCII documentation; ensure consistency across data, code, documentation, and dissemination products and escalate unresolved quality concerns to project leadership.