BS, MS, or PhD in a quantitative field such as Mathematics, Statistics, or Computer Science.
1-3 years of experience in quantitative finance, systematic trading, or applied ML/data science.
Strong programming skills in Python and familiarity with numerical libraries like NumPy and pandas.
Solid foundation in probability, statistics, and machine learning principles.
Ability to critically assess model assumptions and risks of overfitting.
Experience with AI tools for development and research enhancement.
Genuine curiosity about market dynamics and trading systems.
Responsibilities
Conduct research to develop models that enhance trading strategies.
Design and backtest quantitative trading models from concept to deployment.
Analyze market microstructure and execution quality for new research opportunities.
Collaborate with traders and engineers to implement research findings into trading systems.
Utilize AI tools throughout the research process for data exploration and model testing.
Contribute to the development of research infrastructure, including data pipelines and evaluation frameworks.
Benefits
Comprehensive health, dental, and vision insurance fully covered by the firm.
401(k) plan with a 4% company match.
Unlimited paid time off and sick leave.
Free lunch, coffee, drinks, and snacks provided.
Commuter benefits available.
Monthly happy hours and annual team events.
Full Job Description
What you'll do:
Conduct signal, alpha, and feature research to develop models that improve trading strategy performance
Design, backtest, and iterate on quantitative trading models from ideation through production deployment
Analyze market microstructure, execution quality, and options pricing dynamics to identify new research directions
Collaborate closely with traders and engineers to translate research findings into live trading systems
Use AI tools actively throughout your research workflow - from exploring datasets and generating hypotheses to accelerating code and stress-testing model assumptions
Contribute to research infrastructure: data pipelines, evaluation frameworks, and reproducibility tooling
Qualifications and skills:
BS, MS, or PhD in a quantitative field - Mathematics, Statistics, Physics, Computer Science, Financial Engineering, or similar
1-3 years of research or industry experience in quantitative finance, systematic trading, or applied ML / data science
Strong programming skills in Python; comfort with numerical libraries (NumPy, pandas, SciPy) and data analysis at scale
Solid foundation in probability, statistics, and machine learning
Ability to think rigorously about model assumptions, overfitting risks, and out-of-sample validation
Comfort with AI-assisted development: you actively use LLMs and other AI tools to move faster, think more clearly, and build better
Genuine curiosity about markets, trading, and how quantitative systems interact with real-world price dynamics
Candidates from data science or applied ML backgrounds are welcome - strong modeling instincts and statistical rigor matter more to us than prior finance experience
Why Edgehog:
Small team advantage: Direct access to founders and senior team members from day one
Ownership early: Manage real P&L and make meaningful impact within your first year
Cutting-edge tech: Work with our proprietary models and low-latency trading systems built in-house
Tight feedback loops: Weekly 1-on-1s with your mentor, quarterly reviews with leadership
Chicago-based: Affordable cost of living, vibrant trading community
Benefits:
Comprehensive health, dental, and vision insurance with premiums 100% covered by the firm
401(k) with a 4% company match
Unlimited paid time off and sick leave
Free lunch, coffee, drinks, and snacks
Commuter benefits
Monthly happy hours and annual team events
The base salary range for this position is listed below. Base salary represents just one part of overall compensation; all full-time, permanent roles are eligible for a discretionary bonus and benefits, including items in the above list.
The base salary for this role is 120,000 - 175,000 USD per year
