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Intern Topological Data Analysis Jobs (NOW HIRING)

US MED- EQUIP LLC

Data Analytics Intern

Houston, TX

This internship offers a unique opportunity to gain hands-on experience in data analysis, business intelligence, and strategic analytics within the healthcare industry. The intern will work closely ...

Accuray

Product Data Analysis Intern

Madison, WI · On-site

The Product Data Analyst Intern joins the Measurement & Analysis Group (MAG) and assists in the processes and operating mechanisms for measurement and monitoring of product performance. This role ...

EV Realty

Intern, Data Analytics

San Francisco, CA · On-site

The Role As a Data Analytics Intern you will help build and maintain various analytics tools and ... Develop Python/SQL scripts for data analysis, automation, and supporting analytical workflows

EV Realty

Intern, Data Analytics

San Francisco, CA · On-site +1

The Role As a Data Analytics Intern you will help build and maintain various analytics tools and ... Develop Python/SQL scripts for data analysis, automation, and supporting analytical workflows

Hillpointe

Data Analytics Intern

Winter Park, FL

$20/hr

Data Analytics Intern This is a paid internship offering $20/hour, with hands-on experience in building scalable data systems. The Data Analytics Intern will support our team in building and ...

PACCAR

Data Analyst Intern Fall 2026

Columbus, MS

Proficiency in data analysis tools (e.g., Excel, Power BI, Tableau). * Basic knowledge of SQL and ... S. PACCAR intern, you have a full range of benefit options including: Competitive salary and 401k ...

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How much do intern topological data analysis jobs pay per hour?

As of Jun 1, 2026, the average hourly pay for intern topological data analysis in the United States is $22.50, according to ZipRecruiter salary data. Most workers in this role earn between $17.31 and $24.52 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an Intern in Topological Data Analysis, and why are they important?

To thrive as an Intern in Topological Data Analysis, you need a solid background in mathematics—especially topology and algebra—and programming skills, typically supported by coursework or a degree in mathematics, computer science, or a related field. Familiarity with technical tools such as Python, R, and specialized libraries like GUDHI or scikit-tda is often expected. Strong analytical thinking, attention to detail, and effective communication skills help interns interpret results and collaborate with interdisciplinary teams. These skills are crucial for solving complex data problems and contributing valuable insights to research or applied projects.

What types of projects and responsibilities can an Intern in Topological Data Analysis expect to work on during their internship?

As an Intern in Topological Data Analysis, you can expect to support research and application of topological methods to real-world datasets across fields like biology, computer vision, or finance. Typical responsibilities include cleaning and preprocessing data, implementing algorithms such as persistent homology, visualizing results, and collaborating with team members to interpret findings. You may also contribute to academic papers, present findings in meetings, and help develop tools or software to streamline topological analyses. Interns often work closely with experienced researchers or data scientists, gaining practical exposure to both theoretical concepts and their practical applications.

What is an Intern in Topological Data Analysis?

An Intern in Topological Data Analysis is a student or recent graduate who assists in research and projects involving the application of topological methods to analyze complex data sets. They typically work under the guidance of experienced data scientists or mathematicians, learning how to use tools like persistent homology to extract meaningful patterns from data. Their tasks may include coding, data preprocessing, running experiments, and helping interpret the results. The internship provides hands-on experience at the intersection of mathematics, computer science, and data analysis.

What is the difference between Intern Topological Data Analysis vs Data Analyst Intern?

AspectIntern Topological Data AnalysisData Analyst Intern
Required skillsMathematics, topology, data analysis, programmingStatistics, data visualization, Excel, SQL
Work environmentResearch labs, tech companies, academiaBusiness, finance, marketing departments
Industry usageData science, machine learning, research projectsBusiness insights, reporting, data management

Intern Topological Data Analysis focuses on advanced mathematical techniques like topology to analyze complex data structures, often in research or specialized tech environments. In contrast, Data Analyst Interns typically work with statistical tools and visualization to interpret business data. Both roles require analytical skills, but the focus and tools differ significantly, reflecting their distinct industry applications.

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Intern Topological Data Analysis jobs near you
Infographic showing various Intern Topological Data Analysis job openings in the United States as of May 2026, with employment types broken down into 6% Internship, 6% As Needed, 19% Full Time, and 69% Part Time. Highlights an 34% Physical, and 66% Remote job distribution, with an average salary of $46,809 per year, or $22.5 per hour.

Ph.D. Graduate Intern - Quantitative Portfolio Risk Analytics

Risk Analytics Company

Cambridge, MA • On-site

Full-time

Posted 24 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)