ZipRecruiter

Log In

1

Temporary Topological Data Analysis Jobs (NOW HIRING)

Globalfoundries

Topological Qubit Device Engineer

Malta, NY · On-site

$98K - $176K/yr

Introduction The Quantum Topological Device Engineer (MTS) role is a technical contributor within ... Analyze electrical and physical characterization data to identify key performance drivers, failure ...

Merican

Data Analysis Lead

Morristown, NJ · On-site

Temporary Assignment Work Type: Hybrid, 3-4 days onsite/week * We are seeking a experienced business analytics leader to drive strategic data initiatives in the commercial Life Sciences domain, with ...

next page

Showing results 1-20

All JobsTemporary Topological Data Analysis Jobs

Temporary Topological Data Analysis information

See salary details

$34K

$82.6K

$136K

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

As of Jun 13, 2026, the average yearly pay for temporary 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 cities are hiring for Temporary Topological Data Analysis jobs? Cities with the most Temporary 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 Temporary Topological Data Analysis jobs? States with the most job openings for Temporary Topological Data Analysis jobs include:
Temporary Topological Data Analysis jobs near you

Ph.D. Graduate Intern Quantitative Portfolio Risk Analytics

Risk Analytics Company

Cambridge, MA

Full-time

Posted 6 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 applyespecially 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 Youll 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)