Data Scientist

Rice University•Houston, TX

9h•$120,000 - $130,000•Remote

About The Position

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.

Requirements

Bachelor’s degree
5+ years of experience in data science, analytics, and machine learning
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

Nice To Haves

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)

Responsibilities

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