Data Scientist

Introduction:

University of the People (UoPeople) is the first non-profit, tuition-free, American, accredited, 100% online university. Today, UoPeople has over 270,000 students enrolled from more than 200 countries and territories, including 50,000 students who are refugees. UoPeople is accredited by the WASC Senior College and University Commission (WSCUC).

We believe that higher education is a basic human right and that it can transform not only the lives of students, but also their families’ lives, their communities, their nations, and, by extension, the world. See President Reshef’s TED Talk when he announced the founding of the University.

UoPeople is an innovative university, and we welcome team members who bring creativity and innovation to their roles. We’re a fast-paced organization with remote teams all around the globe. If you’re a self-starter who wants to succeed alongside a passionate team, we’d love to hear from you!

UoPeople is supported by the generosity of individuals and foundations, including the Gates, Hewlett, Ford Foundations, Foundation Hoffmann, and others. The University has been covered by The New York Times, BBC, NPR, Times Higher Education, U.S. News & World Report, and many other leading media outlets. President Reshef’s TED Talk and Nas Daily interview about the University have more than 30 million combined views.

Overview

We are seeking a highly motivated and hands-on Machine Learning Engineer / Data Scientist with strong capabilities across Advanced Analytics, Machine Learning, Data Engineering, and Data Analytics.

This role requires a practitioner who can work across the full analytics lifecycle - from data acquisition and feature engineering to model development, deployment, monitoring, and business adoption. Experience with cloud-based AI/ML platforms. The role will work closely with business stakeholders, data engineers, analysts, and leadership teams to identify opportunities, build advanced analytical solutions, and integrate AI-driven insights into business processes.

ESSENTIAL FUNCTIONS / RESPONSIBILITIES

Advanced Analytics & Data Science

• Partner with business stakeholders to identify opportunities for leveraging advanced analytics, machine learning, and AI to solve business challenges.
• Perform exploratory data analysis (EDA), statistical analysis, hypothesis testing, and feature engineering on large and complex datasets.
• Develop predictive, classification, regression, clustering, recommendation, forecasting, anomaly detection, and optimization models.
• Design and implement customer segmentation, propensity scoring, churn prediction, risk modeling, and operational analytics solutions.
• Translate analytical findings into actionable business recommendations and executive-level insights.Machine Learning Engineering
• Design, develop, train, validate, tune, and deploy machine learning models into production environments.
• Build scalable ML pipelines supporting model training, inference, retraining, and monitoring.
• Implement model versioning, performance monitoring, drift detection, and automated retraining frameworks.
• Develop reusable ML components, feature stores, and model-serving architectures.
• Ensure scalability, reliability, explainability, and maintainability of production AI solutions.MLOps & Deployment
• Understanding of CI/CD pipelines for ML workloads.
• Familiarity with MLflow, Vertex AI Pipelines or equivalent MLOps frameworks.

Generative AI & Intelligent Applications

• Design and implement Generative AI solutions using Large Language Models (LLMs).
• Build Retrieval-Augmented Generation (RAG) solutions integrating enterprise data sources.
• Develop conversational AI, AI assistants, recommendation engines, and intelligent search capabilities.
• Implement prompt engineering, vector databases, embeddings, semantic search, and agentic AI workflows.

KEY COMPETENCIES

Must Have Skill:

• 5–7 years of professional experience in data science, machine learning, analytics engineering, or a closely related field, with hands-on experience in Python and SQL.
• Strong practical knowledge of Python for data preparation, exploratory analysis, feature engineering, model development, automation, and ML workflows.
• Good understanding of data science and machine learning fundamentals, including supervised/unsupervised learning, classification, regression, clustering, feature engineering, model evaluation, and hyperparameter tuning.
• Good understanding of predictive analytics techniques and ability to translate business requirements into measurable ML problems, such as student retention/drop prediction, enrollment forecasting, instructor forecasting, and risk prediction.
• Good understanding of dimensional modeling and data warehouse concepts, particularly Bronze/Silver/Gold architecture.
• Hands-on experience with Google Cloud Run Functions and Cloud Orchestrator / similar experience.

Nice to have Skill:

Data Engineering & Analytics

• Build and maintain data pipelines supporting machine learning and advanced analytics workloads.
• Extract, transform, and integrate data from cloud platforms, APIs, databases, enterprise systems, and external sources.
• Develop robust feature engineering and data preparation pipelines.

QUALIFICATIONS

• Bachelor’s degree in engineering, or related field (or equivalent practical experience).
• Earned a Data Scientist certification from a reputed organization.