Resilience Analytics Specialist

Texas A&M • Houston, TX

13d

About The Position

This position involves designing and implementing advanced statistical and machine learning models, developing interactive visualizations, and supporting data infrastructure to inform research and applied projects. The Data Scientist will collaborate with internal and external stakeholders to support federal- and state-funded initiatives focused on hazard risk, resilience, and mitigation.

Requirements

• 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.

Nice To Haves

• 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).

Responsibilities

• Data Strategy and Management - 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.
• Consulting and Collaboration - 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.
• Fundamental Research and R&D - 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.
• Data Analytics and Machine Learning Workflow Development - 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.
• Organizational Development - 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.

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

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 251-500 employees