Community Resilience / Applied Statistics Research Specialist

Resilient Core Systems, Inc.
Community Resilience / Applied Statistics Research Specialist
Grant-Contingent Research Appointment | Proposed 24-Month Federal Research Project
Resilient Core Systems, Inc. is developing advanced disaster-resilience and recovery technology designed to help government and public-sector organizations better understand, coordinate, measure, and manage recovery following major disasters.
RCS is preparing a proposed 24-month community-resilience research project focused on developing and validating an integrated modeling and measurement framework for disaster-resilience decision-making.
The research is organized around four major work areas:
Integrated Data and System Representation
Community Resilience Metrics and Indicators
AI-Enabled Computational Modeling and Uncertainty
Scenario Testing and Validation
We are seeking a Community Resilience / Applied Statistics Research Specialist to join the proposed research team.
This is not simply a general data analyst or business-intelligence position.
We are looking for a researcher with demonstrated expertise in community-resilience measurement, applied statistics, quantitative research methodology, indicator development and validation, longitudinal analysis, uncertainty, experimental design, and scientific interpretation.
The selected candidate will help determine whether proposed community-resilience measurements are scientifically valid, interpretable, stable across differing communities and hazards, and meaningfully related to observed recovery outcomes.
The technical team has specifically identified the need for a researcher with a demonstrated record in community-resilience measurement, applied statistics, and/or advanced computational modeling to complement the project's technical leadership.
The selected candidate may participate in proposal development and, where appropriate, be identified as proposed project personnel.
Grant-Contingent Appointment
This position is being recruited in connection with a proposed federally funded RCS research project.
Selection or participation in proposal development does not constitute immediate paid employment. The paid research appointment will begin only upon receipt of the applicable federal award, final project authorization, and completion of RCS employment and project requirements.
The proposed project period is 24 months.
Who Should Apply
We are particularly interested in candidates with backgrounds such as:
Community-resilience researchers
Applied statisticians
Disaster-resilience researchers
Quantitative social scientists
Research methodologists
Infrastructure-resilience researchers
Hazard and disaster researchers
Recovery and longitudinal-outcomes researchers
Researchers specializing in resilience metrics, indicators, or measurement science
Researchers with experience in multivariate, longitudinal, Bayesian, survival, or uncertainty-focused methods
University, government, national-laboratory, nonprofit, or private-sector researchers working in disaster resilience, infrastructure systems, community recovery, socioeconomic vulnerability, or related fields
Researchers with peer-reviewed work involving resilience measurement, disaster recovery, public infrastructure, socioeconomic vulnerability, hazard impacts, or related quantitative research
The proposed research is focused on a specific scientific problem: how heterogeneous, evolving, and provenance-preserved disaster observations can be transformed into scientifically valid and interpretable measurements of community resilience while explicitly accounting for data quality, uncertainty, contradictory evidence, and recovery progression.
What You’ll Do
Support development and validation of the project's community-resilience measurement framework.
Lead or support selection of candidate resilience indicators grounded in established literature and authoritative research sources.
Design and conduct statistical analyses evaluating whether proposed indicators meaningfully represent resilience and recovery.
Support content validity, construct validity, statistical validity, and longitudinal outcome validation.
Develop and evaluate normalization approaches, alternative weighting methods, dimensional structures, and composite measures where scientifically justified.
Conduct redundancy testing, sensitivity analysis, and stability analysis.
Evaluate whether conclusions materially change under different indicator transformations or weighting strategies.
Help distinguish meaningful resilience measures from variables that are merely correlated with recovery outcomes.
Develop and compare interpretable statistical baseline models against more advanced computational approaches.
Support regression, survival/time-to-recovery, dimensional-reduction, tree-based, Bayesian, ensemble, or other appropriate analytical methods.
Develop methods for handling missing, incomplete, contradictory, or nonrandom disaster data.
Support uncertainty quantification, calibration, prediction intervals, sensitivity testing, and model abstention when evidence is insufficient.
Contribute to historical hindcasting and validation across different disasters, communities, and hazard types.
Support cross-community and cross-hazard generalizability analysis.
Evaluate whether models and indicators developed using one geography or disaster type remain valid in substantially different environments.
Work closely with the Principal Investigator, geospatial research lead, computational-modeling personnel, technical infrastructure team, and disaster-recovery subject-matter experts.
Help establish quantitative success and failure criteria for research outputs.
Document statistical assumptions, methodology, limitations, validation results, and appropriate-use boundaries.
Contribute, where appropriate, to technical reports, conference materials, peer-reviewed research, and other research dissemination.
The technical plan assigns this function responsibility for indicator validity, experimental design, and scientific interpretation, while the broader methodology includes structured expert review, normalization, dimensional analysis, redundancy testing, sensitivity analysis, longitudinal outcome comparison, and alternative weighting strategies.
Research Focus
The project does not assume that community resilience can be reduced immediately to one universal score.
The proposed methodology initially represents resilience as a multidimensional state incorporating factors such as:
Hazard and exposure
Physical and infrastructure performance
Service continuity
Population vulnerability and adaptive capacity
Economic disruption
Recovery capacity and recovery velocity
Mitigation and residual risk
Measurement confidence
Measurements are expected to be evaluated across pre-event/baseline, event/disruption, and recovery/post-event states, allowing the research team to examine disruption magnitude, recovery trajectory, time to service thresholds, residual impairment, and post-intervention change.
Qualifications
Strong candidates should demonstrate substantial experience in several of the following areas:
Applied statistics
Community-resilience measurement
Disaster or hazard research
Quantitative research methodology
Longitudinal data analysis
Indicator development and validation
Multivariate statistics
Experimental or quasi-experimental research design
Sensitivity analysis
Missing-data methodology
Uncertainty quantification
Model validation
Statistical calibration
Time-to-event or recovery modeling
Socioeconomic or demographic analysis
Infrastructure or community systems research
Reproducible scientific computing
Candidates should be comfortable determining not only whether a model produces accurate results, but whether the underlying measurement itself is valid and scientifically defensible.
The research methodology expressly requires validation at the data, indicator, model, and system-framework levels and recognizes that predictive accuracy alone is not sufficient to establish whether a resilience indicator measures its intended construct.
Preferred Research Credentials
Preference may be given to candidates who can demonstrate one or more of the following:
Ph.D. or advanced graduate degree in statistics, applied statistics, disaster science, resilience, sociology, economics, public policy, civil/infrastructure engineering, geography, data science, operations research, or a closely related quantitative field
Peer-reviewed publication record
Google Scholar, ORCID, university, laboratory, or professional research profile
Demonstrated research involving disaster resilience, community rec

Work arrangement
Yes