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$11K

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$198K

How much do quantitative finance phd internship jobs pay per year?

As of Aug 21, 2026, the average yearly pay for quantitative finance phd internship in the United States is $129,666.00, according to ZipRecruiter salary data. Most workers in this role earn between $116,500.00 and $138,500.00 per year, depending on experience, location, and employer.

What is a quantitative finance PhD internship?

A Quantitative Finance PhD Internship is a temporary position designed for PhD candidates in quantitative fields such as mathematics, statistics, computer science, or financial engineering. Interns apply advanced analytical, statistical, and computational methods to solve complex financial problems, often in areas like risk management, algorithmic trading, or portfolio optimization. They work closely with quantitative researchers and traders, gaining hands-on experience with real-world financial data and modeling techniques. The internship provides exposure to the finance industry, helping interns bridge the gap between academic research and practical financial applications.

What does a quantitative finance PhD intern do?

As a Quantitative Finance PhD Intern, your daily responsibilities often involve developing and testing financial models, conducting statistical and econometric analyses, and collaborating with senior quantitative analysts and researchers. You may be tasked with analyzing large datasets, implementing algorithms, and presenting your results to both technical and business teams. Work is typically project-based, allowing you to contribute to real-world financial challenges and gain hands-on experience in research-driven finance. This environment fosters learning and professional development, offering a unique opportunity to apply your academic knowledge to practical problems and network with industry professionals.

What are the key skills and qualifications needed to thrive in the quantitative finance PhD internship position?

To thrive as a Quantitative Finance PhD Intern, you generally need advanced knowledge of mathematics, statistics, financial theory, and model development, supported by progress towards or completion of a PhD in a quantitative field. Proficiency with programming languages such as Python, R, or MATLAB and experience using financial modeling tools or statistical analysis software are highly valuable. Excellent problem-solving ability, effective communication, and teamwork make candidates stand out in collaborative research and fast-paced environments. These skills ensure interns can contribute to complex quantitative projects, analyze large datasets, and present their findings effectively to both technical and non-technical audiences.

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Quantitative Finance Phd Internship jobs near you
Infographic showing various Quantitative Finance Phd Internship job openings in the United States as of August 2026, with employment types broken down into 94% Full Time, 4% Part Time, and 2% Contract. Highlights an 80% Physical, 8% Hybrid, and 12% Remote job distribution, with an average salary of $129,666 per year, or $62.3 per hour.

Machine Learning Internship - PhD: 2027

SIG Susquehanna

Bala Cynwyd, PA • On-site

$80 - $120/hr

Other

Posted 3 days ago

New

Job description

Overview

Our Machine Learning PhD Internship is a 10-week immersive experience designed for PhD candidates who are passionate about solving high-impact problems at the intersection of data, algorithms, and markets.

As a Machine Learning Intern at Susquehanna, you’ll work on high-impact projects that closely reflect the challenges and workflows of our full-time research team. You’ll apply your technical expertise in machine learning and data science to real-world financial problems, while developing a deep understanding of how machine learning integrates into Susquehanna’s research and trading systems. You will leverage vast and diverse datasets and apply cutting‑edge machine learning at scale to drive data‑informed decisions in predictive modeling to strategic execution.

What You Can Expect
  • Conduct research and develop ML models to identify patterns in noisy, non‑stationary data
  • Work side‑by‑side with our Machine Learning team on real, impactful problems in quantitative trading and finance, bridging the gap between cutting‑edge ML research and practical implementation
  • Collaborate with researchers, developers, and traders to improve existing models and explore new algorithmic approaches
  • Design and run experiments using the latest ML tools and frameworks
  • One‑on‑one mentorship from experienced researchers and technologists
  • Participate in a comprehensive education program with deep dives into Susquehanna’s ML, quant, and trading practices
  • Apply rigorous scientific methods to extract signals from complex datasets and shape our understanding of market behavior
  • Explore various aspects of machine learning in quantitative finance from alpha generation and signal processing to model deployment and risk‑aware decision making
What we’re looking for
  • Currently pursuing a PhD in Computer Science, Machine Learning, Statistics, Physics, Applied Mathematics, or a closely related field
  • Proven experience applying machine learning techniques in a professional or academic setting
  • Strong publication record in top‑tier conferences such as NeurIPS, ICML, or ICLR
  • Hands‑on experience with machine learning frameworks, including PyTorch and TensorFlow
  • Deep interest in solving complex problems and a drive to innovate in a fast‑paced, competitive environment
Why Join Us?
  • Work with a world‑class team of researchers and technologists
  • Access to unparalleled financial data and computing resources
  • Opportunity to make a direct impact on trading performance
  • Collaborative, intellectually stimulating environment with global reach
About Susquehanna

Susquehanna is a global quantitative trading firm powered by scientific rigor, curiosity, and innovation. Our culture is intellectually driven and highly collaborative, bringing together researchers, engineers, and traders to design and deploy impactful strategies in our systematic trading environment. To meet the unique challenges of global markets, Susquehanna applies machine learning and advanced quantitative research to vast datasets in order to uncover actionable insights and build effective strategies. By uniting deep market expertise with cutting‑edge technology, we excel in solving complex problems and pushing boundaries together.

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