Paid Data Analytics Intern – AI & Insights (Spain)

HP Global Services; Sant Cugat del Vallès; Presencial; EUR 10.000 - 17.000; A tiempo parcial

Ventajas ofrecidas por este puesto de trabajo

Mentor assigned

Career development workshops

Lunch at reduced prices

On-site sports center

Free parking or easy train access

Flexible working schedule

Descripción de la vacante

HP Global Services is offering a Data Analyst Intern position at HP Barcelona. You will map and structure KPI data, analyze field issues, and prototype AI-driven insights to improve product quality.

The role requires current university enrollment in Spain and availability from February 2027 through June–July 2027, with an internship agreement from the university. Flexible schedules are offered (part-time 4 hours/day or full-time 8 hours/day) and you will gain hands-on experience across data

Formación

• Currently enrolled in a university in Spain and available for the internship.
• Able to commit to Feb–June/July 2027 and sign an internship agreement.

Responsabilidades

• Map and structure quality data for SIQ KPI & Data Model (UIR, defects, validation).
• Analyze field/customer issues vs validation coverage and propose improvements.
• Prototype automation/AI opportunities for SIQ reporting and quality insights.

Conocimientos

Data analysis

KPI dashboard development

Analytical thinking

English communication

Educación

BSc in Computer Science / Data Science / STEM

MSc in a related STEM field

Herramientas

Excel

Power BI

Python

SQL

Descripción del empleo

HP Global Services is offering a Data Analyst Intern position at HP Barcelona. You will map and structure KPI data, analyze field issues, and prototype AI-driven insights to improve product quality.

The role requires current university enrollment in Spain and availability from February 2027 through June–July 2027, with an internship agreement from the university. Flexible schedules are offered (part-time 4 hours/day or full-time 8 hours/day) and you will gain hands-on experience across data