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Graduate Data Science Intern Jobs in Boston, MA (NOW HIRING)

Lutron Electronics Co., Inc

Data Scientist

Boston, MA · On-site

$120K/yr

Graduate degree in Data Science, Computer Science, Statistics, or a related field * Minimum, cumulative GPA of 3.00 for all degrees earned * 4+ years of hands-on experience in data science, analytics ...

Bosch Group

Business Intelligence Intern - Fall

Watertown, MA · On-site

$17.25 - $21.50/hr

The intern will gain hands-on experience working with real business data and contributing to ... Currently enrolled in a Graduate program in business analytics, data science, information systems ...

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All JobsGraduate Data Science Intern JobsGraduate Data Science Intern Jobs in Boston, MA

Graduate Data Science Intern information

See Boston, MA salary details

$13

$24

$45

How much do graduate data science intern jobs pay per hour?

As of Jul 19, 2026, the average hourly pay for graduate data science intern in Boston, MA is $24.45, according to ZipRecruiter salary data. Most workers in this role earn between $18.80 and $26.63 per hour, depending on experience, location, and employer.

What does a Graduate Data Science Intern do?

A Graduate Data Science Intern assists data science teams by analyzing large datasets, developing predictive models, and supporting data-driven decision-making. They often work with programming languages like Python or R, and use tools such as SQL, Excel, and machine learning libraries. Interns contribute to real-world projects, gain hands-on experience in data analysis, and help communicate findings to stakeholders. This role serves as a bridge between academic learning and practical application in the workplace.

What types of projects do Graduate Data Science Interns typically work on, and how do these projects support their professional development?

Graduate Data Science Interns often work on real-world data analysis projects such as building predictive models, cleaning and visualizing data, or assisting with machine learning algorithm implementation. These projects are usually part of larger team initiatives and provide interns with hands-on experience using industry-standard tools and methodologies. Collaborating closely with data scientists, engineers, and business stakeholders, interns gain exposure to the end-to-end data science workflow and receive mentorship to support their growth. This experience not only enhances their technical skills but also helps them develop problem-solving and communication abilities crucial for future career advancement.

What are the key skills and qualifications needed to thrive as a Graduate Data Science Intern, and why are they important?

To thrive as a Graduate Data Science Intern, you need foundational knowledge in statistics, machine learning, and data analysis, typically supported by a degree in a quantitative field. Familiarity with programming languages like Python or R, experience with data visualization tools, and understanding of databases are often required. Strong problem-solving abilities, curiosity, and effective communication help interns collaborate and present insights clearly. These skills enable interns to extract actionable information from data and contribute value to team projects.

What is the difference between Graduate Data Science Intern vs Data Analyst Intern?

AspectGraduate Data Science InternData Analyst Intern
Required CredentialsTypically pursuing or recently completed a degree in Data Science, Statistics, or related fieldOften pursuing or recently completed a degree in Data Analysis, Business, or related field
Work EnvironmentResearch-focused, data modeling, machine learning projects, collaborative teamsData collection, cleaning, reporting, visualization tasks
Employer & Industry UsageTech companies, finance, healthcare, academiaRetail, marketing, consulting, finance

The Graduate Data Science Intern role typically involves working on machine learning models and advanced analytics, requiring a background in data science or related fields. In contrast, Data Analyst Interns focus more on data cleaning, visualization, and reporting. Both roles are entry-level, often in similar industries, but differ in technical depth and project scope.

What are popular job titles related to Graduate Data Science Intern jobs in Boston, MA? For Graduate Data Science Intern jobs in Boston, MA, the most frequently searched job titles are:
What job categories do people searching Graduate Data Science Intern jobs in Boston, MA look for? The top searched job categories for Graduate Data Science Intern jobs in Boston, MA are:
What cities near Boston, MA are hiring for Graduate Data Science Intern jobs? Cities near Boston, MA with the most Graduate Data Science Intern job openings:
Graduate Data Science Intern jobs near you
Infographic showing various Graduate Data Science Intern job openings in Boston, MA as of July 2026, with employment types broken down into 5% Internship, 57% Full Time, 33% Part Time, and 5% Temporary. Highlights an 95% In-person, and 5% Remote job distribution, with an average salary of $50,854 per year, or $24.4 per hour.

Ph.D. Graduate Intern - Quantitative Portfolio Risk Analytics

Risk Analytics Company

Cambridge, MA • On-site

Full-time

Re-posted 12 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 apply—especially 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 You’ll 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)