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Comity

Quantitative Researcher for Congestion Revenue Rights

Reposted 5 Days Ago
Be an Early Applicant
Remote or Hybrid
Hiring Remotely in CA
160K-200K Annually
Senior level
Remote or Hybrid
Hiring Remotely in CA
160K-200K Annually
Senior level
The role involves designing and deploying strategies for congestion revenue rights, applying quantitative methods, and developing machine learning models.
The summary above was generated by AI
🎯 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 and Caffeinated Capital, are aligned to our policy objectives and platform vision.

The Role

Comity is looking for a Quantitative Researcher for Congestion Revenue Rights to help us design, deploy, and operate autonomous, systematic strategies that realize economic value from congestion auctions, using our future information (forecasts) and energy systems models, under real-world market constraints. You’ll

  • Build a market-leading CRR book leveraging our proprietary technologies

  • Architect autonomous strategies end to end — model, forecasts, market actions

  • Work end-to-end from information development to production-ready code if resources are otherwise occupied

We're excited about you because:
  • You have 5+ years of experience applying quantitative methods to FTRs, PTP/UTC products, and/or congestion forecasting in ERCOT.

  • You have applied stochastic optimization to problems in financial or electrical engineering, operations research, or economics.

  • You have conceptual fluency with probability theory and good taste for shaping and managing distributions.

  • You have an advanced degree in quantitative finance, computer science, statistics, machine learning, operations research, or a related quantitative field.

  • You are experienced and comfortable developing and monitoring machine learning models.

  • You are a skilled programmer in Python.

    More importantly —

    • You are extraordinarily driven — you’re relentless in getting research artifacts into production and willing and capable to work end-to-end (forecasts, models, optimization, production-quality code) when supporting resources are contended.

    • You love winning — while you prioritize well, there’s no task too tedious if it drives performance.

    • You are predisposed to collaboration — we maintain a highly open and discursive environment which rewards the refinement of everyone’s ideas and requires genuine curiosity.

Location

We have hubs in Chicago, New York City, 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.

Top Skills

Convex Optimization
Machine Learning
Python
Statistical Learning

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