Quantitative Researcher at DRW

Chicago, Illinois, United States

DRW Logo
Not SpecifiedCompensation
Entry Level & New GradExperience Level
Full TimeJob Type
UnknownVisa
Finance, Trading, Quantitative FinanceIndustries

Requirements

  • A degree in a technical discipline with a focus on statistics, machine learning, signal processing, optimization and control graduating between December 2025 and August 2026 (Bachelor’s, Master’s, PhD)
  • A curiosity for model development and experience handling large data sets
  • Expertise in programming using Python, R, MATLAB, or C++ for conducting research
  • Can advocate for your beliefs in a concise and effective way with the team
  • Significant hands-on experience applying machine learning algorithms to real world problems
  • Strong problem-solving and statistics skills
  • The proactive ability to take the lead on assignments and deliver practical research results in a timely manner

Responsibilities

  • Develop mathematical models using advanced statistical learning methods to build automated trading strategies across multiple asset classes
  • Conduct quantitative analysis of market data to uncover relationships and identify historical trends
  • Extract predictive signals from financial data through both traditional statistical analysis methods and cutting edge machine learning techniques
  • Assist software developers to translate research strategies into production software
  • Optimize the order execution and risk management of our trading system
  • Create robust solutions to problems presented in the trading environment
  • Formulate and apply mathematical modeling techniques to enhance existing trading strategies and perform innovative new research with the goal of identifying and capturing trading opportunities
  • Automate human-decision based trading strategies; prototype algorithmic trades from trading ideas

Skills

Python
R
MATLAB
C++
Machine Learning
Statistics
Signal Processing
Optimization
Quantitative Analysis
Data Analysis

DRW

Proprietary trading and risk management firm

About DRW

RGM Advisors engages in proprietary trading using its own capital to operate in various financial markets. The firm focuses on developing trading strategies that utilize advanced technology and data analytics to manage risk and pursue high returns. By serving institutional clients such as hedge funds and investment banks, RGM Advisors maintains a flexible business model that allows it to trade its own money, rather than clients' funds. This approach enables better risk management and the ability to generate revenue through trading profits, achieved by employing sophisticated algorithms and market-making strategies. RGM Advisors is distinguished by its global presence and its commitment to attracting top talent, providing them with the tools needed to excel and rewarding exceptional performance.

Chicago, IllinoisHeadquarters
2001Year Founded
M_AND_ACompany Stage
Quantitative Finance, Financial ServicesIndustries
1,001-5,000Employees

Benefits

Daily catered breakfast & lunch
Massages
Social events
Gym subsidy
Flexible work arrangements
Monthly tastings
Game room
On-site yoga classes and meditation
Employee led affinity groups
Mentor/mentee outings
Trivia nights
Educational opportunities
DRW-sponsored sports teams
Poker tournament
Private mother's suite

Risks

Increased competition from algorithmic trading firms may erode DRW's market share.
Cryptocurrency market volatility poses risks to DRW's crypto-assets strategy.
Talent war in tech and finance sectors may impact DRW's ability to retain top talent.

Differentiation

DRW combines technology, research, and risk management for diversified trading opportunities.
They operate using their own capital, allowing quick pivots to capture opportunities.
DRW has expanded into real estate, venture capital, and crypto-assets.

Upsides

Machine learning algorithms enhance predictive accuracy and risk management in trading.
DeFi platforms offer new opportunities for higher returns in crypto-assets.
Quantum computing revolutionizes trading strategies with faster, complex data analysis.

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