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Statistical Programmer Intern Jobs in Quincy, 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 Programmer Intern JobsStatistical Programmer Intern Jobs in Quincy, MA

Statistical Programmer Intern information

See Quincy, MA salary details

$88.8K

$154.9K

$261.8K

How much do statistical programmer intern jobs pay per year?

As of Aug 6, 2026, the average yearly pay for statistical programmer intern in Quincy, MA is $154,871.00, according to ZipRecruiter salary data. Most workers in this role earn between $131,400.00 and $168,200.00 per year, depending on experience, location, and employer.

What does a statistical programmer intern do?

A Statistical Programmer Intern supports data analysis and reporting for clinical trials or research projects by writing and validating statistical programs, often using software like SAS, R, or Python. They collaborate with statisticians and data managers to ensure the accuracy and integrity of datasets and outputs. Interns may also assist in creating tables, listings, and figures for regulatory submissions or publications, providing valuable hands-on experience in the field of biostatistics or data science.

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

To thrive as a Statistical Programmer Intern, you need a solid understanding of statistics, programming (especially in SAS, R, or Python), and basic data analysis, often supported by progress toward a degree in statistics, mathematics, computer science, or a related field. Familiarity with statistical software, data visualization tools, and version control systems like Git is often expected. Attention to detail, problem-solving abilities, and strong communication skills help interns effectively manage data tasks and collaborate with team members. These competencies are vital for ensuring accurate data processing, clear reporting, and successful contribution to research or clinical projects.

What types of projects do statistical programmer interns typically work on, and how do these contribute to larger team objectives?

Statistical Programmer Interns often assist in tasks such as cleaning and analyzing clinical or research data, creating data visualizations, and writing code for statistical analyses under the supervision of experienced programmers. These projects contribute to larger team objectives by supporting data-driven decision-making, ensuring data integrity, and helping to prepare reports for regulatory submissions or publication. Interns usually collaborate closely with biostatisticians, data managers, and other programmers, gaining exposure to real-world challenges and learning how their work fits into broader organizational goals.
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Statistical Programmer Intern jobs near you

PhD Graduate Intern Quantitative Portfolio Risk Analytics

RiskAnalytics

Cambridge, MA • On-site

Other

Posted 4 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)

Apply