Data Scientist

This position is not eligible for employment visa sponsorship. Applicants must be authorized to work in the United States on a full-time, ongoing basis without the need for sponsorship now or in the future.

Special Instructions to Applicants: 

Applicants should attach a resume and cover letter in PDF format to the Supporting Documents section of the application.

About Rice:

Boasting a 300-acre tree-lined campus in Houston, Texas, Rice University is ranked among the nation’s top 20 universities by U.S. News & World Report. Rice has a 6-to-1 undergraduate student-to-faculty ratio, and a residential college system, which supports students intellectually, emotionally and culturally through social events, intramural sports, student plays, lectures series, courses and student government. Developing close-knit, diverse college communities is a strong campus tradition, which is why Rice is highly ranked for best quality of life and best value among private universities. 

Rice is also a wonderful place to work. Rice faculty, staff and students share values that are essential to our success as a healthy community. Those values guide our decisions and behaviors and shape Rice’s culture. They come through in the way we treat each other and the welcome we extend to our visitors. These values can be recalled simply by our name — RICE — Responsibility, Integrity, Community and Excellence.

Position Summary:
The Institutional Data, Evaluation, Analytics and Strategy (IDEAS) office is seeking a Data Scientist to bridge the gap between complex data and institutional action. Working with the Associate Provost, you will lead analytical projects spanning the entire student lifecycle—from enrollment and retention to graduation and post‑graduate outcomes—focusing on predictive modeling for student success and long‑term outcome analysis. We are looking for a technical expert who can move past traditional reporting to drive strategy, benchmarking, and policy. We are looking for a professional who is passionate about using data to improve institutional effectiveness and the overall student and alumni experience.

Ideal Candidate Statement:
The ideal candidate is a technically strong and strategically focused Data Scientist with expertise in statistical modeling and machine learning applied to student success and longitudinal outcomes. Has experience developing predictive models (classification, regression, forecasting, time‑to‑event) and translating complex data into clear, actionable insights for senior leadership. Proficient in Python and SQL, can build scalable data pipelines, integrate multi‑source datasets, and operationalize models while maintaining strong data governance standards. The ideal candidate thrives in cross‑functional settings, takes ownership of projects from design through deployment, and communicates analytical findings in ways that inform strategy, equity, and institutional policy.

Workplace Requirements:
This position is fully remote, permitting all tasks to be completed from any location within the United States. Working hours will remain Central Standard Time. Per Rice policy 440, work arrangements may be subject to change.

Hiring Range: This is a full‑time, benefits‑eligible position, and the proposed salary range is $120,000–$130,000 depending on qualifications and experience.
*Exempt (salaried) positions under FLSA are not eligible for overtime.

Minimum Requirements:

• Bachelor’s degree

• 5+ years of experience in data science, analytics, and machine learning

Skills:

• Technical Expertise:

  • Strong programming skills in Python and SQL

  • Sound knowledge of statistical methods and machine learning algorithms

  • (classification, regression, and forecasting)

  • Mastery of advanced Excel for data synthesis and financial/demographic reporting

• Knowledge of open‑source or commercial data visualization tools (Highcharts.js, Plotly, Power BI, Tableau) and data visualization theory

• Ability to think creatively about human behavior and communicate quantitative results clearly to diverse audiences

Preferences:

• 5–8+ years of experience in data science, analytics, and machine learning

• Proven track record of:

  • Deploying ML models to production

  • Leading end‑to‑end data science projects

  • Working with cross‑functional teams

• PhD in a quantitative discipline or Social Sciences with a heavy emphasis on data‑driven research

• Experience in Higher Education or People Analytics, specifically working with longitudinal data or outcomes analysis

• Experience operationalizing machine learning models in cloud environments (AWS, Spark, Hadoop, Hive)

Essential Functions:

Advanced Student Lifecycle Analytics & Longitudinal Modeling

• Predictive Architectures: Design, train, and deploy machine learning pipelines to quantify factors influencing student success, utilizing time‑to‑event analysis and multi‑stage classification for retention and graduation forecasting

• Post‑Graduate Outcomes Optimization: Develop comprehensive frameworks to analyze longitudinal success metrics, utilizing multi‑source data to model career trajectories, graduate placement, and long‑term economic mobility

• Algorithmic Fairness & Equity Analytics: Apply advanced statistical methods and algorithmic fairness frameworks to detect and mitigate performance variance across diverse demographics, ensuring equitable outcomes throughout the student journey

Strategic Analytics Scoping & Cross‑Functional Integration

• End‑to‑End Project Ownership: Lead complex data science initiatives from initial problem definition and feature selection through validation, deployment, and iterative refinement

• Domain‑Agnostic Modeling: Collaborate with cross‑functional campus units to engineer analytical solutions for diverse domains, including financial aid optimization, enrollment forecasting, and human capital analytics

Data Engineering, ETL & Analytics Orchestration

• Complex Pipeline Architecture: Architect and manage scalable ETL/ELT workflows to integrate high‑dimensional datasets from disparate sources, including Student Information Systems (SIS), Learning Management Systems (LMS), and external alumni databases

• Workflow Automation & MLOps: Transition ad‑hoc analytical scripts into production‑ready, reproducible pipelines to enable persistent, real‑time monitoring of institutional KPIs and model performance

• Data Governance & Security: Implement robust data integrity protocols and automated validation checks to ensure high‑fidelity datasets while maintaining strict compliance with FERPA and institutional security standards

Interactive Intelligence & Executive Translation

• BI Infrastructure & Custom Visualization: Develop full‑stack, interactive visualization suites and custom analytical tools using Power BI, Tableau, or web‑based libraries (Highcharts.js, Plotly) to democratize data‑driven intelligence for university leadership

• Technical Storytelling: Synthesize high‑dimensional statistical results and algorithmic outputs into concise, high‑impact narratives that translate technical complexity into strategic recommendations for senior stakeholders

Rice University HR | Benefits: https://knowledgecafe.rice.edu/benefits

Rice Mission and Values: Mission and Values | Rice University

Rice University is committed to ensuring Equal Employment Opportunity and welcoming the fullness of diversity into our candidate pools. Rice considers qualified applicants for employment without regard to race, color, religion, age, sex, sexual orientation, gender identity, national or ethnic origin, genetic information, disability, or protected veteran status. Rice also provides reasonable accommodations to qualified persons with disabilities. If an applicant requires a reasonable accommodation for any part of the application or hiring process, please get in touch with Rice University’s Human Resources Office via email at facstaffada@rice.edu for support.

If you have any additional questions, please email us at jobs@rice.edu. Thank you for your interest in employment with Rice University.