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

Jj

Principal Scientist Data Science

Cambridge, MA

Data Analytics & Computational Sciences Job Sub Function: Data Science Job Category: Scientific/Technology All Job Posting Locations: Barcelona, Spain, Cambridge, Massachusetts, United States of ...

Novartis

Associate Director, Data Science

Cambridge, MA · Hybrid

$160K - $297K/yr

We are seeking an experienced Data Science leader to advance data-driven drug discovery and development by integrating advanced analytics, machine learning, and mechanistic modelling approaches. In ...

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All JobsData Science Analyst Intern JobsData Science Analyst Intern Jobs in Boston, MA

Data Science Analyst Intern information

See Boston, MA salary details

$13

$24

$45

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

As of Aug 12, 2026, the average hourly pay for data science analyst 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 data science analyst intern do?

A Data Science Analyst Intern assists with analyzing large datasets to uncover patterns, trends, and insights that support business decisions. They typically work alongside data scientists and analysts to clean, organize, and visualize data, as well as help build and test predictive models. Interns may also assist in preparing reports and presentations that communicate findings to stakeholders. This role provides hands-on experience with data analysis tools and techniques, and is a valuable stepping stone for a career in data science.

What are some typical projects or tasks that a data science analyst intern might work on during their internship?

As a Data Science Analyst Intern, you can expect to work on projects such as cleaning and analyzing large datasets, building and testing predictive models, and creating visualizations to communicate findings to stakeholders. Interns often collaborate closely with data scientists, engineers, and business analysts, gaining exposure to real-world data challenges and learning how to apply statistical and machine learning techniques. The role usually involves using tools like Python, R, and SQL, and you may also have opportunities to present your work to different teams, helping you develop both technical and communication skills.

What are the key skills and qualifications needed to thrive as a data science analyst intern, and why are they important?

To thrive as a Data Science Analyst Intern, you need a solid grasp of statistics, data analysis, and programming languages such as Python or R, typically supported by coursework or a degree in a quantitative field. Familiarity with data visualization tools like Tableau, SQL databases, and version control systems (e.g., Git) is often required. Problem-solving ability, attention to detail, and strong communication skills help interns effectively interpret data and share insights with team members. These skills are crucial for transforming raw data into actionable business insights and supporting data-driven decision-making.

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

AspectData Science Analyst InternData Analyst Intern
Required SkillsBasic programming, statistical analysis, data visualizationData manipulation, Excel, basic SQL
Work EnvironmentTech companies, startups, research labsBusiness, finance, marketing sectors
Typical Duration3-6 months internship3-6 months internship

The Data Science Analyst Intern role focuses on applying statistical and programming skills to analyze complex data sets, often involving machine learning and predictive modeling. In contrast, Data Analyst Interns primarily handle data cleaning, reporting, and visualization to support business decisions. Both roles are entry-level, require similar foundational skills, and are common in tech and business industries. Understanding these differences helps candidates target their applications effectively.

What are popular job titles related to Data Science Analyst Intern jobs in Boston, MA? For Data Science Analyst Intern jobs in Boston, MA, the most frequently searched job titles are:
What job categories do people searching Data Science Analyst Intern jobs in Boston, MA look for? The top searched job categories for Data Science Analyst Intern jobs in Boston, MA are:
What cities near Boston, MA are hiring for Data Science Analyst Intern jobs? Cities near Boston, MA with the most Data Science Analyst Intern job openings:
Data Science Analyst Intern jobs near you
Infographic showing various Data Science Analyst Intern job openings in Boston, MA as of August 2026, with employment types broken down into 1% As Needed, 82% Full Time, 12% Part Time, and 5% Contract. Highlights an 87% Physical, 4% Hybrid, and 9% 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 7 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)