Data Scientist

George Mason University•Fairfax, VA

5d•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

Data Management & Engineering
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; and
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).
Data Quality & Governance
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); and
Supports privacy, security, and ethical AI practices (e.g., PII handling, FERPA/GDPR awareness, model fairness and bias assessment).
Analytics & Modeling
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.; and
Translates complex findings into clear narratives, visualizations, and recommendations tailored to executive, operational, and technical audiences.
Campus Engagement
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; and
Other duties as assigned.