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Texas A&M University · 11 hours ago

Resilience Analytics Specialist

Houston, TX

Full-time

Onsite

Senior Level, Lead/Staff

8+ years exp

Texas A&M University is committed to enriching the learning and working environment by promoting a culture that respects all perspectives. The Resilience Analytics Specialist will design and implement advanced statistical and machine learning models, develop interactive visualizations, and support data infrastructure for research and applied projects focused on hazard risk and resilience.

Higher Education

Growth Opportunities

No H1B

U.S. Citizen Only

Responsibilities

Develop and implement a research data and information management strategy to support applied and academic projects. Design and manage large-scale research databases, incorporating geospatial and temporal data related to natural hazards

Select, install, and configure foundational database systems, visualization software, and cloud-based infrastructure (e.g., Azure, AWS, Databricks). Develop tools and pipelines for cleaning, transforming, and integrating datasets from multiple sources. Establish standards and criteria for data quality, storage, and usage to enhance project efficiency and reproducibility

Serve as a point of contact for data requests, analytics consultations, and statistical modeling across internal and external teams. Guide stakeholders through data interpretation, predictive model outputs, and spatial insights. Document project needs, timelines, and deliverables while maintaining open communication with collaborators. Support proposal development and data-driven strategy in state and federally funded initiatives. Participate in meetings, trainings, proposals. Contribute to mentoring, outreach, publications

Lead fundamental research in disaster analytics and disaster AI, with emphasis on geospatial-temporal modeling, multimodal data fusion (e.g., imagery, text, sensors), uncertainty quantification, and robustness to extremes. Design and execute experiments; build open, reusable datasets/benchmarks and evaluation protocols for hazard risk modeling. Publish peer-reviewed papers; present findings at conferences and sponsor meetings; contribute to white papers and technical standards. Co-develop competitive proposals (e.g., NSF, NOAA, DHS, NASA) and advance a long-term research agenda aligned with institute goals. Mentor students, research staff, and collaborators on research methods, experiment design, and reproducibility

Architect and maintain end-to-end ML pipelines (ingestion, labeling, feature engineering, training, evaluation, deployment) for disaster-focused use cases. Implement MLOps practices (version control, experiment tracking, model registries, containerization, CI/CD) using cloud platforms (Azure, AWS, Databricks). Develop performant inference services and APIs to deliver models to decision-support tools, dashboards, and partner systems. Monitor data drift, bias, and model performance; implement retraining and documentation to ensure reliability, ethics, and compliance. Ensure reproducibility and knowledge transfer through clear documentation and automation. Apply geospatial analysis for hazard mapping. Support decision-making tools with spatial data. Develop/manage APIs or systems for delivering analytical outputs. Design and implement statistical and ML models. Build and evaluate supervised/unsupervised models. Maintain analytical pipelines in Python/R

Lead strategic planning around data infrastructure and analytics to improve organizational efficiency. Assess and enhance internal data workflows, ensuring alignment with research goals and stakeholder needs. Identify opportunities to acquire or generate new data assets and establish data partnerships with external agencies. Support a culture of data-informed decision making within the institute

Qualification

Statistical analysisMachine learningData visualizationData managementR programmingPython programmingSQLCloud platformsGeospatial analysisInterpersonal skillsCommunication skillsOrganizational skillsCollaboration

Required

Bachelor's degree in applicable field or equivalent combination of education and experience

Eight years of related experience

Strong academic background in statistics, computer science, engineering, mathematics, or similar discipline

Significant knowledge in research and applications of Data Science in one or more operational domains and associated disciplines including but not limited to: transportation, mobility, facilities management, environmental sensing, image and video interpretation, geospatial data processing

Ability to multi-task and work cooperatively with others

Strong interpersonal and communication skills

Proficiency in applying advanced statistical analyses and developing predictive models for natural hazard-related data

Experience with data visualization tools and programming languages (e.g., R, Python, SQL)

Experience with data management and data analytics in cloud-based platforms (e.g., Microsoft Azure, Databricks, Amazon Web Service)

Demonstrated ability to work collaboratively across teams, institutions, and agencies

Strong written communication, analytical, and organizational skills

Ability to train, validate, apply machine learning models to complex data sets

Preferred

PhD preferred in Data Science, Computer Science & Engineering, or Geospatial AI & Engineering (or a closely related field)

Experience developing geospatial AI workflows and conducting research on geospatial AI in the context of disasters

Experience developing research proposals and developing collaborations with faculty and researchers across organizations

Significant ability to answer questions using advanced statistical methods

Significant knowledge of querying data from relational databases using SQL

Significant knowledge and ability to use R or Python to develop analytical solutions

Significant knowledge of data wrangling, data cleaning and prep, dimensionality reduction

Significant knowledge of GIS tools and systems; Big Data concepts, tools, and architecture (e.g., Cloud, Hadoop, Pig, Hive, Spark)

Data visualization skills and ability to present technical solutions to non-technical audience

Ability to cultivate and maintain professional working relationships with people

Research experience related to meteorological risk, disaster mitigation, and/or hazard impacts

Experience working on federally and/or state-funded research grants or contracts

Experience developing/supporting web-based mapping applications and information systems

Knowledge of disaster resilience issues in Texas and the Gulf Coast region

Experience with geospatial analysis and spatial database management, and related tools and languages (e.g.,GDAL; PostgreSQL/PostGIS)

Benefits

Health, dental, vision, life and long-term disability insurance with Texas A&M contributing to employee health and basic life premiums

12-15 days of annual paid holidays

Up to eight hours of paid sick leave and at least eight hours of paid vacation each month

Automatically enrollment in the Teacher Retirement System of Texas

Health and Wellness: Free exercise programs and release time

Professional Development: All employees have access to free LinkedIn Learning training, webinars, and limited financial support to attend conferences, workshops, and more

Employee Tuition Assistance and Educational Release time for completing a degree while a Texas A&M employee

Company

Texas A&M University

Glassdoor4.4

Texas A&M University has a proud history that stretches back to 1876 when The Agricultural and Mechanical College of Texas became the first public institution of higher learning in the state of Texas.

Founded in 1876

College Station, TX, US

10001+ employees