Associate Data Scientist

The Department of Population and Public Health Sciences at the University of Southern California (USC) invites applications for an Associate Data Scientist position supporting research in environmental health, exposomics, air pollution, and geospatial methods for modeling environmental and extreme weather-related exposures and their health impacts across the life course. This position offers opportunities for interdisciplinary collaboration and professional development, including co-authorship on publications, presentation at scientific conferences, and involvement in grant-funded research projects. Areas of scientific investigation include methods development around extreme weather and health, electronic medical record mining, large scale geospatial, remote sensing, and multimodal data integration and analysis, mobility and activity space analysis, and standardization of exposomics data pipelines. Additional areas of interest may include the joint modeling of multiple exposures and interactions with community- and individual-level adaptation and resilience factors on health outcomes. The successful candidate will use analytical, statistical, and programming skills to collect, analyze, and interpret large and complex data sets, and will assist with identifying data-analytic problems, determining the correct data sources, and developing solutions. Individuals with strong technical skills in statistical programming, data modeling, and machine learning – particularly as applied to exposure science, geospatial analysis, remote sensing, or population health – will be given strong consideration. The ideal candidate is a well-rounded, detail-oriented individual who can work independently but also thrives in and contributes to interdisciplinary, collaborative team settings. Responsibilities include but are not limited to: Create, analyze and interpret large datasets to support research goals and solve data analytic problems Build, test, and optimize statistical, predictive, and machine learning models Develop and deploy data pipelines, workflows, and automated processes especially geospatial and remote sensing based Partner closely with study investigators and staff to identify high-impact data applications and support effective decision-making Apply data science best practices to test, deploy, and iterate on analytic solutions Perform data manipulation, cleaning, and quality assurance to ensure 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 Maintain currency with new tools and methods in geospatial and data science, applying up-to-date techniques to ongoing research Maintain technical documentation and support continuous process improvement 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 data sets especially spatial data sets (e.g. Python, ESRI suite) Experience working with and creating data models and data architecture, and using data visualization tools (e.g., Tableau, ArcGIS, D3.js) Knowledge of current data modeling tools and machine-learning techniques (e.g., clustering, decision-tree learning, artificial neural networks) Proficiency with query languages (e.g., SQL, MDX) and experience with relational (e.g., MySQL, SQL Server, Snowflake, Redshift) and non-relational (e.g., MongoDB, NoSQL) databases and cloud environments Knowledge of applied statistical concepts and techniques (e.g., distributions, statistical testing, regression) Ability to write high-quality, well-documented SAS/Python code, with familiarity in unit testing, source control, and code review Excellent written and oral communication skills, including the ability to summarize findings for both technical and non-technical audiences Demonstrated interest in data science and artificial intelligence; open-domain contributions (e.g., GitHub) and/or published writing in data science are a plus Minimum Qualifications 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 The annual base salary range for this position is $99,823.75 - $100,000.00. When extending an offer of employment, the University of Southern California considers factors such as (but not limited to) the scope and responsibilities of the position, the candidate’s work experience, education/training, key skills, internal peer equity, federal, state, and local laws, contractual stipulations, grant funding, as well as external market and organizational considerations. Minimum Education: Bachelor's degree

Addtional Education Requirements Combined experience/education as substitute for minimum education Minimum Experience: 2 years

Minimum Skills: Experience using statistical computer languages (e.g., R, Python, SQL) to manipulate data and draw insights from large data sets. Experience working with and creating data models and data architecture, and using data visualization tools (e.g., Tableau, ArcGIS, D3.js). Knowledge of current data modeling tools and various machine-learning techniques and algorithms (e.g., clustering, decision-tree learning, artificial neural networks). Experience scripting and programming in several languages with common data science toolkits. Proficient use of query languages (e.g., SQL, MDX) and experience working with relational (e.g., MySQL, SQL Server, Oracle, Snowflake, Redshift) and non-relational (e.g., Mongo, NoSQL) databases. Knowledge of applied, statistical concepts and techniques skills (e.g., distributions, statistical testing, regression). Excellent written and oral communication skills. 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, and the ability to uncover root cause and evaluate different solution options. Preferred Education: Bachelor's degree In Applied Mathematics Or Computer Science Or Statistics Or in related field(s)

Preferred Experience: 4 years Preferred Skills: Experience in data science, analytics, IT, cognitive engineering, or related fields. Demonstrated interest in data science and artificial intelligence, and experience in open domain (e.g., GitHub). Published writing on artificial intelligence and/or data science. Ability to write high-quality Python code. Familiarity with unit testing, source control and code review.

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