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Statistical Programming Intern Jobs in Bedford, MA

DataRobot, Inc.

Agentic AI Intern

Boston, MA · On-site

Each AI Native Intern is embedded in a real team, working on real problems, and is expected to ... Statistics, Engineering, or a related quantitative field * Working proficiency in Python (pandas ...

DataRobot

Agentic AI Intern

Boston, MA · Remote

Each AI Native Intern is embedded in a real team, working on real problems, and is expected to ... Statistics, Engineering, or a related quantitative field * Working proficiency in Python (pandas ...

DataRobot

Agentic AI Intern

Boston, MA · Remote

Each AI Native Intern is embedded in a real team, working on real problems, and is expected to ... Statistics, Engineering, or a related quantitative field * Working proficiency in Python (pandas ...

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All JobsStatistical Programming Intern JobsStatistical Programming Intern Jobs in Bedford, MA

Statistical Programming Intern information

What does a statistical programming intern do?

A Statistical Programming Intern assists in analyzing data and creating statistical reports, often using software languages like SAS, R, or Python. They help design and implement data analysis plans, support senior statisticians or data scientists, and ensure data quality and integrity. Their work is crucial in fields like clinical research, finance, and marketing, where data-driven decision-making is essential. Interns gain hands-on experience with real-world datasets and learn best practices in data management and statistical analysis.

What is the difference between Statistical Programming Intern vs Data Analyst Intern?

AspectStatistical Programming InternData Analyst Intern
Required SkillsProgramming (R, SAS, Python), statistical methodsData analysis, Excel, SQL, visualization
Work EnvironmentPharmaceutical, biotech, or research labsBusiness, marketing, or finance sectors
Typical TasksData cleaning, statistical modeling, programmingData interpretation, reporting, dashboards

Both roles often require programming and data skills, but Statistical Programming Interns focus more on statistical modeling and programming tasks within research environments, while Data Analyst Interns handle broader data analysis and reporting in business settings. They share similar entry-level requirements but differ in industry focus and daily responsibilities.

What types of projects or tasks can a statistical programming intern expect to work on during their internship?

As a Statistical Programming Intern, you will typically assist with data cleaning, preparation, and analysis for ongoing research or clinical studies. You might be responsible for writing and testing code in SAS, R, or Python to generate tables, listings, and figures, as well as supporting the development of statistical analysis plans. Interns often collaborate closely with biostatisticians, data managers, and clinical teams to ensure high-quality data outputs. This role offers valuable exposure to real-world datasets and best practices in statistical programming, providing a solid foundation for future roles in data science or biostatistics.

What are the key skills and qualifications needed to thrive as a statistical programming intern, and why are they important?

To thrive as a Statistical Programming Intern, you need a solid grounding in statistics, programming (often with R or SAS), and data analysis, typically supported by coursework or a degree in statistics, mathematics, or a related field. Familiarity with statistical software, version control systems like Git, and database tools is highly beneficial, alongside any relevant certifications in programming languages or analytics platforms. Attention to detail, analytical thinking, and effective communication are vital soft skills for interpreting data and collaborating with team members. These skills are important because they enable interns to accurately analyze data, contribute to research projects, and support informed decision-making in data-driven environments.

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Statistical Programming Intern jobs near you
Infographic showing various Statistical Programming Intern job openings in Bedford, MA as of August 2026, with employment types broken down into 1% Internship, 78% Full Time, 13% Part Time, 1% Temporary, 6% Contract, and 1% Nights. Highlights an 89% Physical, 3% Hybrid, and 8% Remote job distribution.

PhD Graduate Intern Quantitative Portfolio Risk Analytics

RiskAnalytics

Cambridge, MA • On-site

Other

Posted 13 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)

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