Graduate Data Science & Predictive Analytics Intern

Highmark Health; Yuma (AZ); Sur place; USD 28 000 - 34 000; Plein temps

Avantages offerts par ce poste

Mentorship program

Résumé du poste

Highmark Health invites applicants to participate in the Summer 2027 Predictive Modeling & Data Science Graduate Internship. The program offers hands-on experience in advanced analytics, data science, statistical methods, and machine learning to support business decisions.

Interns work with data scientists and analysts to design, develop, and evaluate analytics solutions, gain exposure to the end-to-end lifecycle, and build skills in data visualization, storytelling, and presenting findings to

Qualifications

• Pursuing graduate degree in Data Science, Statistics, Analytics, Mathematics, Computer Science or related quantitative discipline.
• Coursework, research, or project experience involving predictive modeling, machine learning, statistical analysis, or advanced analytics.
• Experience with data preparation, exploratory data analysis, feature engineering, and model evaluation.

Responsabilités

• Conduct exploratory and statistical analysis and support predictive model development.
• Prepare and transform data and evaluate model results.
• Create data visualizations and communicate findings to technical and business audiences.

Formation

Bachelor’s degree

Pursuing a graduate degree (full or part-time) in Data Science, Statistics, Analytics, Mathematics, Computer Science or related quantitative discipline

Outils

Programming basics

Description du poste

Highmark Health invites applicants to participate in the Summer 2027 Predictive Modeling & Data Science Graduate Internship. The program offers hands-on experience in advanced analytics, data science, statistical methods, and machine learning to support business decisions.

Interns work with data scientists and analysts to design, develop, and evaluate analytics solutions, gain exposure to the end-to-end lifecycle, and build skills in data visualization, storytelling, and presenting findings to