Associate Data Scientist

Support research in environmental health and exposure science by building data pipelines, predictive models, and geospatial analytics at USC.

• Create, analyze, and interpret large datasets to address data analytic problems and research needs
• Build, test, and optimize statistical, predictive, and machine learning models
• Develop and deploy data pipelines, workflows, and automated processes, with emphasis on geospatial and remote sensing
• Partner with study investigators and staff to identify high-impact data applications and support decision-making
• Apply data science best practices to test, deploy, and iterate on analytic solutions
• Perform data manipulation, cleaning, and quality assurance to maintain data integrity
• Monitor project progress, track activities, and provide regular updates to PI and study team members on status and deliverables
• Draft reports, figures, and presentations summarizing analytic methodology and findings
• Stay current on new tools and methods in geospatial and data science and apply up-to-date techniques to ongoing research
• Maintain technical documentation and support continuous process improvement

Requirements

• Bachelor’s degree in Applied Mathematics, Geography, Computer Science, Statistics, or a related quantitative field (or equivalent experience)
• Minimum 2 years of experience in data science, data analytics, or a related field
• Proficiency in Python and SQL for data analysis and data manipulation
• Experience working with relational and/or non-relational databases (e.g., MySQL, SQL Server, Snowflake, MongoDB)
• Experience performing data cleaning, transformation, and validation
• Demonstrated ability to analyze datasets and deliver actionable insights
• Strong written and verbal communication skills
• Experience using statistical computer languages (e.g., R, Python, SQL) to manipulate data and derive insights from large datasets
• Experience working with and creating data models and data architecture
• Experience using data visualization tools (e.g., Tableau, ArcGIS, D3.js)
• Knowledge of current data modeling tools and machine learning techniques/algorithms (e.g., clustering, decision-tree learning, artificial neural networks)
• Experience scripting and programming in several languages using common data science toolkits
• Proficient use of query languages (e.g., SQL, MDX)
• Knowledge of applied statistical concepts and techniques (e.g., distributions, statistical testing, regression)
• Ability to provide both detailed information and summaries to management-level individuals and groups
• Experience developing customer relationships and delivering customer-focused service
• Proven problem-solving and decision-making skills, including the ability to uncover root cause and evaluate solution options

Preferred Qualifications

• Master’s degree or PhD in Geography, Statistics, Environmental Health, Remote Sensing, or a related field
• 5+ years of relevant work experience in exposure data science, geospatial analytics, or a related field
• Experience using statistical computer languages (e.g., SAS, R, Python, SQL) to manipulate data and draw insights from large datasets, especially spatial datasets (e.g., Python, ESRI suite)
• Experience writing high-quality, well-documented SAS/Python code, with familiarity in unit testing, source control, and code review
• Excellent written and oral communication skills, including summarizing findings for both technical and non-technical audiences
• Demonstrated interest in data science and artificial intelligence, including open-domain contributions (e.g., GitHub) and/or published writing in data science

Technologies

• Python, SQL, R, SAS
• ESRI suite (ESRI), Tableau, ArcGIS, D3.js
• MySQL, SQL Server, Snowflake, Redshift
• MongoDB, Mongo, NoSQL
• Oracle, MDX
• Clustering, decision-tree learning, artificial neural networks

Location: Los Angeles, CA (onsite)

Salary: USD 99,823 - 100,000 per year

• Minimum Education: Bachelor’s degree
• Minimum Experience: 2 years
• Additional Education Requirements: Combined experience/education as substitute for minimum education