Hybrid Data Science Intern: Build Models & Insights

Hewlett Packard Enterprise Company; Spring (TX); Hybrid; USD 48,000 - 55,000; Full time

Benefits offered by this job

Health & Wellbeing

Career development programs

Job summary

Hewlett Packard Enterprise is seeking a Data Science Intern to work in a hybrid setup, averaging 2 days per week in an HPE office. You will engage in data analysis, model building, and visualization to translate insights into actionable outcomes.

The role targets students pursuing a Bachelor's or Master's in Data Science, CS, or related IT fields, with foundational knowledge in analytics and ETL tools. Collaboration across teams is encouraged.

Qualifications

• D pursuing a Bachelor's or Master's degree in Data Science, CS, or related IT field.
• Fundamental knowledge of data science methodologies.
• Basic data mapping and data migration skills.
• Familiarity with analytics software (R, SAS, SPSS, Python).
• Understanding of machine learning concepts and ETL tools.
• Good communication and presentation skills.

Responsibilities

• Participates in the analysis and validation of data sets/solutions/user experience.
• Aids in the development, enhancement and maintenance of a client's metadata based on analytic objectives.
• Contributes to building models for the overall solution, validates results and performance.
• Supports the research, identification and delivery of data science solutions to problems.
• Supports visualization of the model's insights, user experience and configuration tools for the analytics model.

Skills

Data science basics

Python

R

SAS

SPSS

SQL

Education

Bachelor's/Master's in IT related field

Tools

Informatica

Ab Initio

Talend

Job description

Hewlett Packard Enterprise is seeking a Data Science Intern to work in a hybrid setup, averaging 2 days per week in an HPE office. You will engage in data analysis, model building, and visualization to translate insights into actionable outcomes.

The role targets students pursuing a Bachelor's or Master's in Data Science, CS, or related IT fields, with foundational knowledge in analytics and ETL tools. Collaboration across teams is encouraged.