ZipRecruiter

Log In

1

Topological Data Analysis Jobs (NOW HIRING)

next page

Showing results 1-20

All JobsTopological Data Analysis Jobs

Topological Data Analysis information

See salary details

$34K

$82.6K

$136K

How much do topological data analysis jobs pay per year?

As of Jun 29, 2026, the average yearly pay for topological data analysis in the United States is $82,640.00, according to ZipRecruiter salary data. Most workers in this role earn between $62,500.00 and $97,000.00 per year, depending on experience, location, and employer.

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

To thrive in Topological Data Analysis, you need a strong background in mathematics (especially algebraic topology), statistical analysis, and data science, often supported by an advanced degree in math, computer science, or a related field. Proficiency with programming languages like Python or R, and familiarity with topological data analysis (TDA) tools such as GUDHI or Ripser, are highly valuable. Critical thinking, curiosity, and effective communication help you translate complex topological findings into actionable insights for interdisciplinary teams. These skills ensure accurate interpretation of complex data shapes and facilitate meaningful contributions to projects in fields like bioinformatics, finance, or machine learning.

What is a Topological Data Analysis job?

A Topological Data Analysis (TDA) job involves applying concepts from topology, a branch of mathematics, to analyze and extract insights from complex data. Professionals in this field use techniques like persistent homology and mapper algorithms to uncover hidden structures in high-dimensional datasets. They often work in industries such as bioinformatics, finance, and machine learning, helping to interpret data patterns that traditional methods might miss. TDA specialists typically have expertise in mathematics, data science, and programming, using tools like Python, R, and specialized libraries such as Gudhi or Ripser.

How does Topological Data Analysis typically collaborate with other departments or teams within an organization?

Professionals working in Topological Data Analysis often collaborate closely with data scientists, domain experts, and software engineers to translate abstract topological results into practical solutions. You may contribute to interdisciplinary research, provide insights during project planning meetings, and help interpret complex data structures uncovered through TDA methods. Regular communication and collaborative problem-solving are essential, as you’ll frequently explain technical concepts to non-specialists and incorporate their feedback into analysis workflows. This collaborative environment fosters innovation and ensures that TDA findings are effectively integrated into broader analytical and business strategies.

More about Topological Data Analysis jobs
What are the most commonly searched types of Topological Data Analysis jobs? The most popular types of Topological Data Analysis jobs are:
What states have the most Topological Data Analysis jobs? States with the most job openings for Topological Data Analysis jobs include:
What job categories do people searching Topological Data Analysis jobs look for? The top searched job categories for Topological Data Analysis jobs are:
Topological Data Analysis jobs near you
Infographic showing various Topological Data Analysis job openings in the United States as of June 2026, with employment types broken down into 100% Full Time. Highlights an 95% In-person, and 5% Hybrid job distribution, with an average salary of $82,640 per year, or $39.7 per hour.

Ph.D. Graduate Intern - Quantitative Portfolio Risk Analytics

Risk Analytics Company

Cambridge, MA • On-site

Full-time

Posted 23 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)