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Permanent Topological Data Analysis Jobs (NOW HIRING)

Collabera

Data Analysis

Eagan, MN · On-site

... Staffing & Solutions) Permanent Placement Services and Vendor Management Programs. Collabera ... Experiencein a Business Analyst or related role involving data analysis StrongExcel skills ...

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Permanent Topological Data Analysis information

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$34K

$82.6K

$136K

How much do permanent topological data analysis jobs pay per year?

As of Sep 7, 2026, the average yearly pay for permanent 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 is the difference between Permanent Topological Data Analysis vs Data Scientist?

AspectPermanent Topological Data AnalysisData Scientist
Required credentialsBackground in mathematics, topology, and data analysis; often advanced degreesDegree in computer science, statistics, or related fields; often requires programming skills
Work environmentResearch-focused, analytical, often in academia or specialized data firmsBusiness-oriented, collaborative, in tech companies, finance, or consulting
Industry usagePrimarily in data analysis, research, and academiaAcross various industries including tech, finance, healthcare, and marketing

Permanent Topological Data Analysis focuses on advanced mathematical techniques to analyze data structures, while Data Scientists apply a broader set of skills including statistics, programming, and domain knowledge to interpret data for business insights. Both roles require strong analytical skills but differ in their core methodologies and work environments.

What cities are hiring for Permanent Topological Data Analysis jobs?

Cities with the most Permanent Topological Data Analysis job openings:

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 Permanent Topological Data Analysis jobs?

States with the most job openings for Permanent Topological Data Analysis jobs include:

Permanent Topological Data Analysis jobs near you

Ph.D. Graduate Intern - Quantitative Portfolio Risk Analytics

Risk Analytics Company

Cambridge, MA • On-site

Full-time

Re-posted 2 days ago

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

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)