Quantitative Research

Quantitative Research Associate

SIG’s Quantitative Research Associates hold advanced degrees from diverse fields including mathematics, physics, computer science, and engineering. Our quants apply their problem solving expertise to real-time trading opportunities within the capital markets.

“I was given an ongoing project in high-performance trading, with the opportunity to take ownership of the existing process. I worked on improving it and eventually converting the existing process into a test bed for future ideas. I was given a lot of leeway to develop the ideas I had in whatever directions seemed promising.”
– SIG Options Quantitative Researcher

At SIG, you will work alongside traders and software engineers to develop, test, and implement predictive models, pricing models, and technical tools. You will utilize your quantitative ability and technical skill set to design strategies essential to SIG’s trading performance.

“There's a level of openness and collaboration that I believe is unique to our company, particularly in terms of giving feedback. We know we all make errors, and the best way to improve quality over time is to be open to feedback.”
– SIG Algo Quantitative Researcher

Education

You will attend classes taught by experienced traders to learn about capital markets, decision science, and game theory. This theoretical training is reinforced through mentoring provided by successful quants at SIG who will help you bridge the gap between your academic experience and the trading world.

Apply Here

Apply Here

Quant Internship

Internship

The goal of our 10-week summer programprogramme is to give you real-world working experiences that apply quantitative research to the firm’s trading activity. You will work on essential projects for the firm while gaining a better understanding of how we approach the markets.

Intern Education

Our quant interns participate in educational programsprogrammes customized to their unique position at SIG. Classes provide an introduction to the products SIG trades and the finance industry. They evolve into challenging quantitative courses that expose the application of quantitative theory to real-world problems that we face as contributors to the US open markets. Interns are also mentored by a Quantitative Researcher at SIG while spending time on our trade floor gaining hands-on experience. The education programprogramme is designed to advance knowledge of our business, current industry topics, and the application of quantitative theories in finance.

Intern Perks

  • Relaxed dress code: jeans and sneakers are the norm, shorts all summer long
  • Food, beverages, and snacks available all day
  • A 9,000 square-foot gym with cardio, cross fit and strength machines, plus yoga and indoor cycling classes
  • Discounts for dining, entertainment, shopping, travel, and attractions
  • Social events including a poker tournament, dinners, and sporting events in Philadelphia
  • On-site Wellness Center staffed with full-time Nurse Practitioner
  • On-site services such as barber, dry cleaning and laundry, auto repair and detailing, and ATM
  • Free housing is provided and conveniently located near SIG

Like earning a doctorate, a successful career at SIG requires a relentless pursuit of knowledge, creative thinking, and extraordinary attention to detail.

As a PhD at SIG, you will work in an environment which is challenging, fast-paced, and competitive. Whether the application is time series modelling or reinforcement learning, you will apply academic rigor to transform your research into concrete results work with state-of-art techniques in statistics and optimisation. Success at SIG relies on both a strong technical foundation and effective collaboration between our quantitative researchers, technologists, and traders. New hires will start working right away on a project in proprietary trading, with the guidance and mentorship of a senior member of our team.

Full-time Opportunities

Quantitative Researchers are dedicated to the data-driven development and improvement of our trading models at SIG. Our Quantitative Researchers apply their problem-solving skills and mathematical creativity to create solutions in today’s competitive and constantly changing financial markets. In this role, you will be highly involved in the day-to-day discussions that shape our trading activities and transform quantitative methods into trading opportunities. Machine Learning Engineers design, develop, and deploy algorithms used to optimise SIG’s trading activities. You will join a team of engineers and researchers who specialise in the application of machine learning techniques to the modelling and trading of financial instruments. Combining cutting-edge research with real industry data, our Machine Learning Engineers build tools that directly impact the financial markets.

Apply Here

Apply Here

Your Impact

At SIG, you will work alongside traders and technologists to develop, test, and implement predictive models, pricing models, and technical tools. You will utilise your quantitative ability and technical skill set to design strategies essential to SIG’s trading performance.

“I was given an ongoing project in high-performance trading, with the opportunity to take ownership of the existing process. I worked on improving it and eventually converting the existing process into a test bed for future ideas. I was given a lot of leeway to develop the ideas I had in whatever directions seemed promising”
– SIG Options Quantitative Researcher

Education

You will attend classes taught by experienced traders to learn about capital markets, decision science, and game theory. This theoretical training is reinforced through mentoring provided by successful quants at SIG who will help you bridge the gap between your academic experience and the trading world. Our Quantitative Researchers have an opportunity to spend time learning & working in US offices as part of the graduate program.

“There's a level of openness and collaboration that I believe is unique to our company, particularly in terms of giving feedback. We know we all make errors, and the best way to improve quality over time is to be open to feedback.”
– SIG Algo Quantitative Researcher