Data Scientist

George Mason University•Fairfax, VA

22h•Hybrid

About The Position

The Data Scientist will design and deliver data-driven solutions that enable stakeholders to make timely, evidence-based decisions. This role blends statistical modeling, machine learning, and storytelling with robust data engineering practices and data governance. This role integrates data from enterprise systems (e.g., Banner, Salesforce) with external sources (e.g., IPEDS, SCHEV) to support enrollment, student success, budgeting, advancement, accreditation, and compliance reporting. The Data Scientist is comfortable working with complex, multi-source datasets; builds reproducible analytics; and collaborates across technical and functional teams to translate insights into measurable impact.

Requirements

Master’s in Data Science, Statistics, Computer Science, Economics, Applied Math, or related field or the equivalent combination of education and practical experience
Experience with data visualization (e.g., SAS Viya, Tableau, Power BI) and clear, executive-friendly storytelling
Demonstrated experience with complex joins, data normalization, and resolving data quality issues
Experience with Banner, Canvas, Salesforce, and integrating external datasets (e.g., IPEDS, SCHEV)
Demonstrated experience deploying statistical and machine learning models
Demonstrated experience scoping problems, eliciting requirements, and designing analytics in partnership with key stakeholders
Proficiency in Python (pandas, NumPy, scikit-learn, statsmodels) and SQL
Knowledge and strong understanding of experimental design, sampling, statistical inference, and model evaluation metrics
Demonstrated knowledge of data privacy, data security, and ethical AI practices (e.g., PII handling, FERPA/GDPR awareness, model fairness and bias assessment)
Demonstrated skill in building reusable data pipelines that clean and integrate datasets from multiple sources and that support production grade analytics and monitoring
Demonstrated ability to create data validation routines for completeness, accuracy, consistency, and/or other data quality dimensions
Demonstrated ability to translate complex findings into clear narratives, visualizations, and recommendations
Demonstrable excellence in written, verbal, and interpersonal communication skills

Nice To Haves

PhD or equivalent experience in a quantitative discipline
Experience working in an institutional research/effectiveness setting in higher education

Responsibilities

Acquires, cleans, and integrates complex datasets from varied sources (data warehouses, external data sources via APIs, flat files, enterprise systems), ensuring quality, lineage, and reproducibility
Builds reusable data pipelines (e.g., Python, SQL, R) and version-controlled notebooks that support production-grade analytics and monitoring
Identifies gaps and ensures data are incorporated into institutional data architecture necessary to maintain and/or enhance analytic and reporting data models (e.g., enrollment, retention, completion)
Establishes and maintains data validation routines and automated checks for completeness, accuracy, consistency, and timeliness in partnership with campus constituents and steward offices
Profiles data, identify root causes of anomalies, and collaborates with data stewards/custodians on corrective actions and standards implementation
Contributes to metadata repositories, data catalogs, and governance documentation (data dictionaries, business definitions, lineage)
Supports privacy, security, and ethical AI practices (e.g., PII handling, FERPA/GDPR awareness, model fairness and bias assessment)
Develops, validates, and deploys statistical and machine learning models to study complex phenomenon of import to the university, e.g. student behavior, cost analyses, etc.
Translates complex findings into clear narratives, visualizations, and recommendations tailored to executive, operational, and technical audiences
Scopes problems, elicits requirements, and designs analytics that align with strategic priorities in partnership with colleagues across campus
Assists in dashboard and report development to ensure interpretation of data is clear when using self-service tools
Maintains cutting edge knowledge on methods and tools that contribute to advancing data science and responsible use
Other duties as assigned