Quantitative Researcher for Risk and Research Engagement

🎯 WHY WE EXIST

We’re on a mission to improve the reliability, transparency, and efficiency of our energy systems, fostering a future with sustainable and abundant energy. To accomplish our aims, we’re leveraging state of the art statistical learning and convex optimization methods (AI) to build the financial rails of our future energy systems that will accelerate the deployment of clean energy resources.

We envision energy systems that are efficient, autonomous, resilient, and powered by 100% renewable energy.

🧗 WHO WE ARE

Our founders (ex-Apple, Bluevine; ex-Affirm, Square, Google) are Stanford alumni with experience in complex systems, machine learning and structured finance. Our world-class investors, Maverick Ventures https://www.maverickventures.com/ and Caffeinated Capital https://caffeinatedcapital.com/, are aligned to our policy objectives and platform vision.

We have hubs in New York City, Chicago, and San Francisco.

THE ROLE

This role sits at the intersection of portfolio management, quantitative research, and risk management. You will partner closely with Portfolio Managers, Risk Managers, and senior leadership to develop risk models, analyze power market dynamics, improve portfolio allocation decisions, and build the analytical infrastructure that supports our trading and risk platform.

You will work directly with decision-makers across the organization to deepen our understanding of market structure, portfolio behavior, and risk drivers in U.S. power markets. This role is highly collaborative, analytical, and hands-on, with significant opportunity to shape our risk and portfolio analytics capabilities as the platform scales.

In this role, you will:

- Analyze U.S. power markets to identify market opportunities, portfolio risks, and drivers of performance

- Partner directly with Portfolio Managers and Risk Managers to support portfolio construction, allocation decisions, and alpha research

- Improve backtesting, experimentation, and simulation infrastructure to drive research outcomes

- Develop quantitative models for risk analysis, scenario analysis, and performance attribution

- Track and analyze portfolio P&L and exposures, delivering actionable insights to PMs and senior leadership

- Support investment and risk decision-making under uncertainty by combining quantitative analysis and sound judgment

- Collaborate closely with engineering, research, and leadership teams to scale Comity’s trading and risk platform

WE’RE EXCITED ABOUT YOU BECAUSE:

- You have strong quantitative foundations in statistics, optimization, probability, machine learning, or applied mathematics

- You have experience developing quantitative models for portfolio analytics, risk management, or trading applications

- You have experience with performance attribution, portfolio optimization, or systematic trading analytics

- You are comfortable influencing Portfolio Managers, Risk Managers, and senior stakeholders in fast-moving environments

- You are a strong Python programmer with experience building analytical tooling and working with large datasets

- You have strong intuition for markets, portfolio behavior, and risk under changing market conditions

- You communicate quantitative insights clearly to both technical and non-technical audiences

- You are intellectually rigorous and operationally resilient; you dig into messy problems and drive them to resolution

Nice to have

- Experience in U.S. wholesale electricity markets, including virtual trading, congestion modeling, nodal pricing, or FTRs

- Advanced degree in a quantitative discipline such as mathematics, statistics, computer science, engineering, physics, or economics

LOCATION

We have hubs in New York City, Chicago, and San Francisco.

At Comity, we seek to recruit, develop, and retain the most talented people from a diverse candidate pool. Our priority is to ensure that all applicants are provided with fair and equal access to employment opportunities. Recruiting and hiring decisions are made without regard to race, color, religion, sex, national origin, age, disability, or any other class protected by law.