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Quantitative Systematic Trader – PhD: 2027

Susquehanna International Group, LLP
Posted 3 months ago, valid for 11 days
Location

New York, NY 10008, US

Salary

$300,000 per year

Contract type

Part Time

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Sonic Summary

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  • Susquehanna is seeking a Quantitative Systematic Trader who will leverage quantitative research and trading intuition to develop automated trading strategies.
  • Candidates must possess a PhD or be a postdoc in quantitative fields and have strong analytical and programming skills, particularly in Python.
  • The role includes responsibilities such as modeling market behavior, optimizing trading strategies, and managing risk in real-time operations.
  • The annual base salary for this position is $300,000, and the firm values a collaborative and intellectually driven work culture.
  • Visa sponsorship is available, and applicants will also be considered for a Quantitative Research position without needing to apply separately.

Overview

At Susquehanna, quantitative systematic traders tackle challenging data and algorithmic problems that inform decision-making in global financial markets. Their work spans signal generation, execution, market microstructure, portfolio construction, risk management, simulation, and large-scale data analysis, drawing from a broad quantitative toolkit to build strategies that trade in live markets.

 

As a Quantitative Systematic Trader, you’ll combine quantitative research expertise with deep trading intuition to build and refine data-driven, fully automated trading strategies. You’ll actively monitor real-time strategy performance and use market behavior and feedback to improve and optimize profitability. Serving as a hybrid between traders and researchers, quantitative systematic traders focus more on alpha monetization, execution efficiency, and live trading performance. While there is some overlap with the Quantitative Researcher role, quantitative systematic traders spend more of their time close to live markets.

 

You’ll have access to Susquehanna’s extensive proprietary datasets and large-scale computing infrastructure—including a rapidly growing cluster of thousands of high-end GPUs—support computationally intensive research, large-scale simulation, and rapid experimentation. Our teams work in small, highly collaborative teams where ideas are debated openly, evidence guides decisions, and promising research can quickly produce practical impact.

 

What You Can Expect

  • Modelling: Apply probability theory, statistical analysis, and machine learning techniques to analyze and interpret market behavior
  • Alpha Monetization: Blend quantitative signals with trading intuition and live market insights to optimize existing strategies or build new ones from the ground up
  • Risk Management: Oversee the deployment and real-time operation of trading strategies, fine-tune parameters, and respond to anomalies as needed
  • Education: Participate in a comprehensive education program and receive personalized mentorship from senior professionals to accelerate your growth
  • Collaboration: Work in an open environment where you’ll collaborate with researchers and technologists to build parts of the execution engine

What we’re looking for

We’re looking for intellectually curious problem-solvers who are excited by difficult quantitative problems and motivated by making decisions under uncertainty.

 

You may be a strong fit if you have:

  • A PhD, graduating by Summer 2027, or postdoctoral experience in computer science, economics, electrical engineering, mathematics, operations research, physics, statistics, or another highly quantitative discipline
  • A strong foundation in probability, statistics, optimization, numerical methods, or quantitative modeling
  • Experience developing research code in Python and working with large datasets; experience with C++, scientific computing, or high-performance computing is a plus
  • Excellent problem-solving skills and an interest in using quantitative models that inform real trading decisions
  • Strong communication skills and an interest in collaborating with traders, researchers, and engineers to refine ideas through discussion and experimentation
  • A track record of intellectual curiosity and competitive drive demonstrated through research, competitions, strategic games, or other analytical pursuits
  • A strong interest in trading and the drive to develop a deep mental model of market microstructure and market intuition
  • The ability to thrive in an environment that values creativity, evidence-based thinking, and continuous learning

 

Visa sponsorship is available for this position.

 

By applying to this role, you will be automatically considered for the Quantitative Researcher position. There is no need to apply to both positions to be considered for both.

 

Opportunities as a Quantitative Researcher and as a Quantitative Systematic Trader will be available in our Philadelphia and New York offices.

 

The annual base pay for this role is $300,000. Susquehanna considers factors such as scope and responsibilities of the position, work experience, education/training, key skills, as well as market and organizational considerations when extending an offer.

 

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.

 

If you're a recruiting agency and want to partner with us, please reach out to recruiting@sig.com. Any resume or referral submitted in the absence of a signed agreement will not be eligible for an agency fee.

 

 

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