Associate Data Scientist (Institutional Research)

Please submit a resume and cover letter as part of your application.

> Key Responsibilities:
• Analyze and interpret medium to large datasets to solve data analytic problems in support of institutional decision making
• Build, test, and optimize statistical, predictive, and machine learning models
• Develop and deploy data pipelines, workflows, and automated processes
• Partner with stakeholders to translate business needs into analytical solutions
• Perform data cleansing, validation, and transformation to ensure data integrity
• Design and evaluate experiments, including A/B testing methodologies
• Create reports, dashboards, and presentations to communicate insights clearly
• Monitor project timelines, deliverables, and stakeholder communications
• Maintain technical documentation and support continuous process improvement

> Preferred Qualifications:
• Master's degree in Data Science, Computer Science, Statistics, or a related field
• Experience building and maintaining ETL pipelines and data workflows
• Experience with workflow orchestration tools such as Apache Airflow (DAG development)
• Experience with machine learning or statistical modeling techniques
• Experience with data visualization tools (e.g., Tableau, Power BI, D3.js)
• Experience working with large-scale or enterprise datasets
• Familiarity with Git, version control, and testing frameworks
• Experience designing data architecture or automated data system

> Minimum Qualifications:
• Bachelor's degree in Applied Mathematics, 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

In addition, the successful candidate must also demonstrate, through ideas, words and actions, a strong commitment to USC's Unifying Values of integrity, excellence, community, well-being, open communication, and accountability.

Salary and Benefits:
The annual base salary range for this position is $99,823 - $$116,656. 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.

To support faculty and staff well-being, USC provides benefits-eligible employees with a broad range of benefits and perks to help protect their and their dependents' health, wealth, and future. These benefits are available as part of the overall compensation and total rewards package. You can learn more about USC's comprehensive benefits here.

Apply Now
If you're excited to solve complex data challenges and contribute to meaningful institutional impact, we encourage you to apply today. Please submit your resume and cover letter to be considered.

#LI-MS1

Job Qualifications
Minimum Education:
-Bachelor's degree
-Combined experience/education as substitute for minimum education

Minimum Experience:
-2 years

Minimum Field of Expertise:
. Experience using statistical computer languages (e.g., R, Python, SQL) to manipulate data and draw insights from
large data sets.
. 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.
. Proven problem-solving and decision-making skills, and the ability to uncover root cause and evaluate different
solution options.
. Experience in data science, analytics, IT, cognitive engineering, or related fields.
. Ability to write high-quality Python code.

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