Campus Graduate I Summer Internship Program - 2027 Data Science, Finance - New York, NY

American Express
New York
Workplace: HybridInternshipUSD 24.05 - 63 hourlyFunction: Data Science & Machine LearningEducation: bachelorsSkills: ["Quantitative problem-solving","Attention to detail","Communication","Ability to explain to technical and non-technical audiences"]

Build predictive models and analytical frameworks using large-scale datasets to forecast key financial metrics and support growth, risk, and profitability decisions. Apply statistical and machine learning techniques to identify drivers of business performance, develop and validate models, and translate complex finance questions into measurable analytical plans. Communicate methodology, assumptions, results, and business implications through executive-ready presentations while partnering with Finance, Business, Risk, and Technology teams.

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FursaFursa
American Express
American Express
5 days ago

Campus Graduate I Summer Internship Program - 2027 Data Science, Finance - New York, NY

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Last checked: 18 hours agoStatus: Live

Job Summary

Build predictive models and analytical frameworks using large-scale datasets to forecast key financial metrics and support growth, risk, and profitability decisions. Apply statistical and machine learning techniques to identify drivers of business performance, develop and validate models, and translate complex finance questions into measurable analytical plans. Communicate methodology, assumptions, results, and business implications through executive-ready presentations while partnering with Finance, Business, Risk, and Technology teams.
Location: New York
Workplace: Hybrid
Employment Type: Internship
Job Function: Data Science & Machine Learning
Seniority: Intern level

Key Responsibilities

  • •Query, transform, and analyze large datasets using SQL, Python, and related analytical tools.
  • •Build, test, and refine predictive models using statistical and machine learning techniques (e.g., regression, classification, clustering, decision trees).
  • •Evaluate model performance, improve predictive accuracy, and document assumptions, limitations, and modeling choices.
  • •Use Python or R for exploratory data analysis, feature engineering, automation, and validation.
  • •Translate finance objectives into analytical plans and present insights, including project purpose, findings, limitations, and business impact.

Pay and Benefits

Salary: USD 24.05 - 63 hourly
Perks:Health InsuranceRemote WorkLearning Budget

Key Requirements

  • •Currently enrolled in a full-time graduate degree program in a quantitative field such as Data Science, Statistics, Computer Science, Engineering, Quantitative Finance, Artificial Intelligence, or applied mathematics.
  • •Expected graduation date between December 2027 and June 2028.
  • •Experience involving predictive modeling, machine learning, statistical analysis, optimization, or applied data science through coursework, research, internship, or projects.
  • •Demonstrated ability to use programming and quantitative methods to solve ambiguous, real-world problems.
  • •Python or R and SQL experience working with large datasets; familiarity with feature engineering, model training, validation, and performance measurement.
Education:Bachelor's
Skills:Quantitative problem-solvingAttention to detailCommunicationAbility to explain to technical and non-technical audiences
Tech Stack:PythonRSQLPower BIMachine learningPredictive modelingStatistical analysisFeature engineeringRegressionClassificationClusteringDecision treesLLM-based AI systems

Eligibility

Work Authorization:Authorization required. Sponsorship not provided.

Company Brief

American Express
Provides global payment, credit, and travel-related financial services for consumers and businesses. Best known for its charge and credit cards, merchant payment network, and premium customer rewards and servicing.
Industry: Payments
Company Size: Enterprise (1,001+ employees)
Revenue: USD 1B+
Growth: Public Company
Valuation: Public Company (Market Cap in USD)
Funding: IPO / Publicly Listed
Headquarters: New York City, United States
Founded: 1850
WebsiteLinkedIn