Paid Media Intern: Digital Campaigns & Analytics

ICF

Maryland City (MD)

Remote

USD 38,000 - 78,000

Full time

4 days ago
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Job summary

ICF is offering a paid media internship designed to provide hands-on experience across media strategy, digital buying, activation, and reporting for regulated portfolios across the United States. The 10-week summer program is full-time, with remote work possible and occasional in-person meetings for those in the DC/MD/VA area or near New York City.

You will work with media strategists, buyers, data analysts, and operations specialists to learn campaign setup, optimization, and performance

Qualifications

  • Must have completed at least 30 credit hours in advertising, marketing (media), analytics, or related field.
  • Preferred: completed bachelor’s degree with enrollment verification in a graduate program.
  • Strong analytical, problem-solving, and communication skills.

Responsibilities

  • Participate in a rotational internship program across media strategy, digital media buying, campaign execution, trafficking, reporting, and operations.
  • Support integrated planning teams in development of paid media strategies and cross-channel media plans.
  • Contribute to deliverables by leveraging research, audience insights, media performance data, and campaign results.

Skills

Analytical skills
Cross-channel execution
Team collaboration
Microsoft Excel
PowerPoint
Campaign optimization

Education

Bachelor’s degree
Graduate enrollment (pursuing advanced degree)

Tools

Google Ads
The Trade Desk
Basis
Meta

Job description

ICF is offering a paid media internship designed to provide hands-on experience across media strategy, digital buying, activation, and reporting for regulated portfolios across the United States. The 10-week summer program is full-time, with remote work possible and occasional in-person meetings for those in the DC/MD/VA area or near New York City.

You will work with media strategists, buyers, data analysts, and operations specialists to learn campaign setup, optimization, and performance

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