Resilience Analytics Specialist

Resilience Analytics Specialist

Agency: Texas A&M University

Department: Institute For A Disaster Resilient Texas

Job Location: Houston, Texas

Job Type: Staff

Our Commitment - Texas A&M University is committed to enriching the learning and working environment by promoting a culture that respects all perspectives, talents & lived experiences. Embracing varying opinions and perspectives strengthens our core values which are:
Respect, Excellence, Leadership, Loyalty, Integrity, and Selfless Service.

The Division of Research is committed to a truly comprehensive university where students, researchers, and inventors bring scholarship and innovation to bear for the benefit of the community, the state, and the nation. Texas A&M’s research creates new knowledge that provides basic, fundamental, and applied contributions resulting, in many cases, in economic benefits to the state, nation and world.

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.

Compensation will be commensurate to the selected hire’s experience.

A cover letter and resume are strongly recommended.

• 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.

• 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; Postgre
  
  SQL/PostGIS).…