Data Science Summer Internship for Undergrads – Hybrid, Paid

L'oreal Usa; New York (NY); Hybrid; USD 37,000 - 41,000; Full time

Benefits offered by this job

Hybrid work policy

Mentorship program

Relocation assistance

Housing assistance

Job summary

L’Oréal USA Summer Internship Program is seeking a Data Science Intern to help transform data into actionable insights across iconic brands. You’ll work at the intersection of analytics, technology and business in a fast-paced, global environment.

This paid internship includes mentorship, professional development, and a hybrid work policy, with housing assistance available based on eligibility.

Qualifications

• Experience with data science languages (Python, R, SQL).
• Understanding of data warehouses, databases, data lakes, and data platforms.
• Familiarity with APIs, data integration, and data quality issues.
• Strong foundation in data management, modeling, integration and governance.

Responsibilities

• Analyze and model large, complex datasets to reveal trends and insights.
• Build data-driven solutions using Python, R, SQL and analytics techniques.
• Apply statistics, ML, and analytics to real-world business challenges.
• Create data stories via visualizations for technical and non-technical stakeholders.
• Work with cloud platforms (AWS, Azure, GCP) and big data tech.
• Support data products with data quality and lifecycle governance.
• Collaborate with cross-functional teams to align solutions with needs.

Skills

Python

R

SQL

Data manipulation

APIs

Data governance

Education

Pursuing Bachelor's degree

Tools

Cloud platforms

Data warehousing

Job description

L’Oréal USA Summer Internship Program is seeking a Data Science Intern to help transform data into actionable insights across iconic brands. You’ll work at the intersection of analytics, technology and business in a fast-paced, global environment.

This paid internship includes mentorship, professional development, and a hybrid work policy, with housing assistance available based on eligibility.