Associate Data Scientist - Environmental Health & Geospatial

University of Southern California; Los Angeles (CA); Hybrid; USD 100,000 - 101,000; Full time

Job summary

University of Southern California seeks an Associate Data Scientist to support environmental health and exposomics research, including geospatial methods, remote sensing, and life-course exposure modeling.

You will develop data pipelines, apply statistical and machine learning techniques, and collaborate with interdisciplinary teams to interpret findings and inform health outcomes across populations.

Qualifications

• Bachelor’s degree required in a quantitative field (e.g., Applied Mathematics, Geography, Computer Science, Statistics).
• Minimum 2 years of data science/data analytics experience.
• Proficiency in Python and SQL for data analysis and data manipulation.
• Experience with relational and/or non-relational databases and data visualization tools.
• Strong communication skills and ability to summarize findings for technical and non-technical audiences.

Responsibilities

• Create, analyze and interpret large datasets to support research goals and solve data analytic problems.
• Build, test, and optimize statistical and machine learning models.
• Develop and deploy data pipelines, workflows, and automated processes (geospatial/remote sensing).
• Collaborate with investigators to identify high-impact data applications and support decision-making.
• Document analytic methods and maintain code quality with testing and version control.

Skills

Python

SQL

R

SAS

Machine learning

Data visualization

Geospatial analysis

MDX

Education

Bachelor's degree

Master’s or PhD

Tools

Tableau

ArcGIS

D3.js

MongoDB

Snowflake

Redshift

MySQL

SQL Server

Oracle

Job description

University of Southern California seeks an Associate Data Scientist to support environmental health and exposomics research, including geospatial methods, remote sensing, and life-course exposure modeling.

You will develop data pipelines, apply statistical and machine learning techniques, and collaborate with interdisciplinary teams to interpret findings and inform health outcomes across populations.