Quantitative Researcher, Algorithmic Trading

GTS; Village of Pelham (NY); Hybrid; USD 150,000 - 225,000; Full time

Benefits offered by this job

Medical, Dental, Vision

401k match

Daily Meal Allowance

Equinox Corporate Membership Rate

Snacks

Office Game Room

Casual Dress

Hybrid work: 4 days in office

Job summary

Global Trading Systems seeks a Quant Researcher to join the algorithmic trading team in New York. You will evaluate, optimize, and produce analytics to improve execution strategies, collaborating with traders and developers to scale production systems.

The role requires strong math/statistics, Python proficiency, and the ability to automate complex tasks in a fast-paced environment. Hybrid work is available with in-office collaboration.

Qualifications

• 1–2 years experience with algorithmic trading systems is preferred.
• Strong mathematics and statistics foundation.
• Excellent programming skills in Python.
• Proven ability to automate and scale processes.

Responsibilities

• Predict intraday quant params from historical data.
• Predict intraday volume patterns and velocity.
• Identify sparse data symbols and create models to handle.
• Design communication model between predictions and trading system.
• Analyze algorithmic outcomes to propose improvements to scheduling and order placement.
• Research alpha models used for pricing, venue selection, and internalization decisions.

Skills

Python

Data analytics

Quantitative techniques

Communication

Teamwork

Attention to detail

Education

B.S./M.S. in STEM

Job description

Overview

The algorithmic trading team at GTS is seeking applications for the role of Quant Researcher. The successful candidate will be part of the team evaluating, optimizing and producing analytics to improve the performance of our algorithmic execution strategies. They will work closely with traders and developers to refine these processes in support of revenue-generating trading algorithms. The algorithmic trading team at GTS produces execution algorithms that execute in the market and internalize flows within GTS. The quantitative researchers represent a critical piece of this operation.

The successful candidate will work with subject matter experts in addition to senior traders and developers. They will develop a deep understanding of algorithmic trading strategies, market microstructure, trading infrastructure, trading system and evaluation metrics. Candidates should be ambitious and motivated. They need to be capable of both designing and implementing processes that can be automated and scaled, handling production problems that arise in real-time, and over time contributing views on how these complex systems should be best organized and managed.

GTS is a collection of financial services companies spanning a wide array of asset classes and investment approaches, all powered by the combination of market expertise with innovative, proprietary technology. With roots as a quantitative trading firm continually building for the future, the GTS family of companies are able to leverage the latest in artificial intelligence systems and sophisticated pricing models to bring consistency, efficiency, and transparency to today’s financial markets. GTS’s electronic market maker GTS Securities accounts for 3-5% of daily cash equities volume in the U.S. and is a leading Designated Market Maker (DMM) at the New York Stock Exchange, responsible for nearly $13 trillion of market capitalization. For more information on GTS, please visit www.gtsx.com.

Responsibilities

Sample Problems/Projects:

• The algorithms produce security specific quantitative params to define how they trade:
  • Predict intraday quant params from historical data.
  • Predict intraday volume patterns and velocity.
  • Identify sparse data symbols and create models to handle.
  • Design communication model between predictions and trading system.
• Analyze algorithmic outcomes to propose improvements to scheduling and order placement.
• Research alpha models used for pricing, venue selection, and internalization decisions.
• Research and propose new internalization models to improve algorithmic PnL.

This is a an opportunity to join a new green field team in our algorithmic trading business. In this role you will be collaborating closely with traders on proprietary trading opportunities.

Qualifications

• 1-2 years experience with algorithmic trading systems and models is preferred.
• B.S./M.S. from a leading university in a STEM discipline.
• Deep understanding and ability in mathematics and statistics.
• Excellent programming skills in Python.
• Strong proficiency in data analytics and quantitative techniques.
• Attention to detail, exceptional organization, and proclivity for automation.
• Excellent communication skills: ability to express complex concepts in simple terms.
• Ability to work independently and as part of a team in a fast-paced environment.

We'reproud toemploy leadingtalentin the industry and work to ensure our employees enjoy a highquality of life.

Please note: The use of AI tools during interviews is strictly prohibited, unless explicitly agreed upon.

In accordance with New York City's Pay Transparency Law, the base salary range for this role is $150,000 to $225,000. Base salary does not include other forms of compensation or benefits.

Benefits:

• Core Benefits: Medical, Dental, and Vision coverage with generous employer contribution for employees and dependents.401k match.
• Perks: Daily Meal Allowance, Equinox Corporate Membership Rate, Snacks, Office Game Room, Casual Dress.
• Hybrid Work Policy: 4 Days a Week in office, Fridays can be worked from home.

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, age, national origin, or protected veteran status, and will not be discriminated againston the basis ofdisability.

Unsolicited resumes: We do not accept unsolicitedheadhunterand agency resumes and will not pay fees to any third-party agency or company that does not have a signed agreement with GTS.