Quantitative Analyst

Habitat Energy•Austin, TX

10h•Hybrid

About The Position

We have a vacancy for a Quantitative Analyst to join our US team based in Austin, Texas. This role will predominantly be based on-site in our Austin office. You will be responsible for: Signal Generation & Market Fundamentals: Bridge advanced machine learning techniques with core market fundamentals to consistently extract alpha. Operationalize the outputs of powerflow models and grid topology to anticipate network congestion, translating complex system dynamics into actionable, high-conviction trading strategies. Process and transform high-dimensional, unstructured ISO market data into robust predictive features. Portfolio Optimization: Build, calibrate, and scale optimization models to support complex, multi-asset trading strategies. Seamlessly integrate strategies across physical and financial energy markets (including spot, futures, and derivatives) to maximize risk-adjusted returns and portfolio scalability. Prototype and deploy robust valuation frameworks for virtual and asset-backed energy trades. Develop dynamic risk profiles that accurately capture market volatility, congestion pricing dynamics, and tail-risk scenarios to ensure optimal capital allocation and downside protection. Performance Attribution & Strategy Refinement: Drive rigorous post-trade analytics to clearly isolate model efficacy from general market performance. Establish a continuous feedback loop of backtesting and quantitative review to refine strategy accuracy, adapt to shifting market regimes, and improve future signal generation.

Requirements

Bachelor’s degree required in Electrical Engineering, Power Systems, Quantitative Finance or a related field.
3+ years pricing structured products or derivatives in energy or financial markets.
Familiarity with Financial Derivatives, Options Greeks, Market Making & Market Microstructure
Solid understanding of ERCOT forwards, and option markets
2+ years of related power systems, electric market design, or energy trading experience.
Knowledge of market fundamentals within ERCOT.
Understanding of transmission systems, power flow, congestion, and curtailment.
Fundamental knowledge of how solar, wind, and battery storage assets operate within the various energy markets.
Working knowledge of SQL and Python
Knowledge of ISO and RTO wholesale market operations and applicable state regulations.
Working knowledge of the various forms of capacity and ancillary services markets.
Proven problem-solving and negotiation skills.
Strong verbal and written communication skills and a high level of attention to detail.
Ability to exercise discretion and independent judgment.

Nice To Haves

Experience managing energy storage resources
Experience structuring products related to carbon offset, long-term financing of renewables or energy storage assets
Experience and confidence presenting to clients, board members and prospective clients.
Experience working with SQL

Responsibilities

Signal Generation & Market Fundamentals
Bridge advanced machine learning techniques with core market fundamentals to consistently extract alpha.
Operationalize the outputs of powerflow models and grid topology to anticipate network congestion, translating complex system dynamics into actionable, high-conviction trading strategies.
Process and transform high-dimensional, unstructured ISO market data into robust predictive features.
Portfolio Optimization
Build, calibrate, and scale optimization models to support complex, multi-asset trading strategies.
Seamlessly integrate strategies across physical and financial energy markets (including spot, futures, and derivatives) to maximize risk-adjusted returns and portfolio scalability.
Prototype and deploy robust valuation frameworks for virtual and asset-backed energy trades.
Develop dynamic risk profiles that accurately capture market volatility, congestion pricing dynamics, and tail-risk scenarios to ensure optimal capital allocation and downside protection.
Performance Attribution & Strategy Refinement
Drive rigorous post-trade analytics to clearly isolate model efficacy from general market performance.
Establish a continuous feedback loop of backtesting and quantitative review to refine strategy accuracy, adapt to shifting market regimes, and improve future signal generation.