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Internship Competitive Analysis Jobs (NOW HIRING)

Blue Nile

Customer Data Strategy Intern

New York, NY · On-site

$17 - $19.50/hr

Present findings at the end of the internship. * Competitive Benchmarking Analysis: Learn how similar brands use a CDP to drive customer acquisition and retention, by researching competitor examples.

Get Engaged

Internship

Atlanta, GA · On-site

$14.50 - $19.25/hr

An internship with Get Engaged Media provides students with practical experience in an agency ... competitive analysis of social media profiles, and other activities surrounding social media ...

Cornerstone Tree Farm

Intern

Dade City, FL · On-site

$13.75 - $17.50/hr

Perform ad hoc analysis and special projects as assigned Qualifications * Currently enrolled in a ... Paid internship -- competitive hourly rate What You Will Gain * Real-world accounting experience in ...

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Loudr Agency

Social Media Strategist

Denver, CO

Market & Competitive Analysis: Execute deep-dive competitive analysis and sentiment tracking to ... Minimum of 3 years of full-time social media strategy experience (excluding internships)

Loudr Agency

Social Media Strategist

Denver, CO · On-site

$60K - $80K/yr

Market & Competitive Analysis: Execute deep-dive competitive analysis and sentiment tracking to ... Minimum of 3 years of full-time social media strategy experience (excluding internships)

Loudr Agency

Social Media Strategist

Denver, CO · On-site

Market & Competitive Analysis: Execute deep-dive competitive analysis and sentiment tracking to ... Minimum of 3 years of full-time social media strategy experience (excluding internships)

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How much do internship competitive analysis jobs pay per hour?

As of May 31, 2026, the average hourly pay for internship competitive analysis in the United States is $15.54, according to ZipRecruiter salary data. Most workers in this role earn between $12.50 and $17.55 per hour, depending on experience, location, and employer.

What is the difference between Internship Competitive Analysis vs Market Research Intern?

AspectInternship Competitive AnalysisMarket Research Intern
Required CredentialsTypically pursuing or recent graduate in business, marketing, or related fieldsSimilar educational background, often with coursework in marketing, statistics, or social sciences
Work EnvironmentCorporate or consulting firms, marketing departmentsMarket research firms, corporate marketing teams, or consulting agencies
Employer & Industry UsageUsed in competitive intelligence, strategic planning, and marketing analysisUsed for consumer insights, product development, and market trend analysis

Internship Competitive Analysis and Market Research Intern roles share similar educational backgrounds and work environments. However, Internship Competitive Analysis focuses specifically on analyzing competitors' strategies and market positioning, while Market Research Interns gather broader consumer data and market trends. Both roles are essential in strategic decision-making but serve different analytical purposes within the industry.

What cities are hiring for Internship Competitive Analysis jobs? Cities with the most Internship Competitive Analysis job openings:
What are the most commonly searched types of Competitive Analysis jobs? The most popular types of Competitive Analysis jobs are:
What states have the most Internship Competitive Analysis jobs? States with the most job openings for Internship Competitive Analysis jobs include:
Internship Competitive Analysis jobs near you

Ph.D. Graduate Intern Quantitative Portfolio Risk Analytics

Risk Analytics Company

Cambridge, MA

Full-time

Posted 23 days ago

Job description

Ph.D. Graduate Intern Quantitative Portfolio Risk Analytics (Cross-Disciplinary)

Position Overview
We are seeking an exceptional Ph.D. graduate student to join our team as a Quantitative Portfolio Risk Analytics Intern. This role focuses on developing and applying advanced analytical methods to understand portfolio risk, market structure, and complex financial systems.
We are intentionally recruiting from cross-disciplinary, research-driven backgrounds. Doctoral candidates from fields such as physics, astrophysics, math, applied mathematics, statistics, engineering, economics, computer science, quantum computing, biotech, and other data-intensive sciences are strongly encouraged to applyespecially those interested in translating rigorous quantitative methods into real-world financial applications.
Key Responsibilities
  • Develop and enhance quantitative models for portfolio risk, including factor-based and statistical approaches
  • Analyze large, high-dimensional financial datasets to uncover structure, dependencies, and sources of risk
  • Design and implement analytical tools and pipelines using Python and SQL
  • Contribute to model validation, backtesting, and performance evaluation
  • Collaborate with risk, engineering, and data teams to improve model scalability and data infrastructure
  • Communicate complex quantitative insights through clear visualizations and technical summaries
  • Apply advanced methodologies from your discipline (e.g., stochastic modeling, optimization, machine learning, or geometric/topological approaches) to improve risk analytics
Required Qualifications
  • Currently enrolled in a graduate Ph.D. program in a highly quantitative field (e.g., Math, Applied Mathematics, Physics, Astrophysics, Statistics, Computer Science, Engineering, Financial Engineering, Economics, Biotech or other data-driven disciplines)
  • Strong foundation in probability, statistics, and numerical methods
  • Proficiency in Python (NumPy, pandas, or similar) and/or SQL
  • Experience working with large datasets and implementing quantitative models
  • Ability to think rigorously about complex systems and translate theory into practical solutions
Preferred Qualifications
  • Familiarity with quantitative finance concepts (e.g., portfolio theory, factor models, volatility modeling, Value-at-Risk)
  • Experience with scientific computing, optimization, or machine learning
  • Background or research in cross-disciplinary areas such as:
    • Statistical physics, complex systems, or network theory
    • Applied or computational mathematics
    • Machine learning or probabilistic modeling
    • Quantum computing or advanced optimization techniques
    • Topological data analysis or geometric data methods
  • Prior research, publications, or project work demonstrating advanced quantitative modeling
What Youll Gain
  • Exposure to real-world portfolio risk problems at the intersection of finance and advanced analytics
  • Opportunity to apply cutting-edge academic methods in a production environment
  • Collaboration with a highly quantitative, cross-disciplinary team
  • Experience working with large-scale financial data and modern analytics infrastructure
  • Mentorship and potential pathway to full-time quantitative roles
Duration & Compensation
  • Internship: Summer 2026, with potential to extend
  • Paid internship (competitive, based on experience and location)