Data Scientist – Hazards & Infrastructure Analytics

Texas A&M Engineering Experiment Station (TEES); College Station (TX); On-site; USD 70,000 - 100,000; Full time

Benefits offered by this job

Medical insurance

Retirement plan

Paid time off

Professional development opportunities

Job summary

Texas A&M Engineering Experiment Station (TEES) in Houston, TX, seeks an Assistant Data Scientist in Civil Engineering to develop and deploy ML models for hazard assessment and resilience analysis. You will build data pipelines, perform feature engineering, and collaborate with researchers to translate prototypes into production-ready tools.

The role emphasizes data-driven decision making, rigorous documentation, and active participation in knowledge transfer and publication efforts.

Qualifications

• Bachelor’s degree or equivalent combination of education and experience.
• Two years of related experience.

Responsibilities

• Develop, train, and evaluate machine learning models for hazard assessment, infrastructure vulnerability analysis, and community resilience metrics.
• Build and maintain data pipelines for ingesting, processing, and integrating multi-source datasets (geospatial, sensor, social, and administrative data).
• Design feature engineering workflows and model evaluation frameworks to ensure robust, reproducible results.
• Collaborate with researchers and domain experts to translate prototype models into production-ready tools.
• Document methodologies, results, and technical processes to support knowledge transfer and publication efforts.
• Perform other duties as assigned.

Skills

Machine learning

Data pipelines

Feature engineering

Collaboration

Documentation

Education

Bachelor's degree or equivalent

Job description

Texas A&M Engineering Experiment Station (TEES) in Houston, TX, seeks an Assistant Data Scientist in Civil Engineering to develop and deploy ML models for hazard assessment and resilience analysis. You will build data pipelines, perform feature engineering, and collaborate with researchers to translate prototypes into production-ready tools.

The role emphasizes data-driven decision making, rigorous documentation, and active participation in knowledge transfer and publication efforts.