Quantitative Portfolio Manager

About the role

SmartestEnergy, a subsidiary of the Marubeni Corporation with offices in the US, UK and Australia, is looking for a Quantitative Portfolio Manager in New York. The role develops, maintains and uses quantitative models to estimate and manage the risks in the company's energy portfolio, supporting Trading and Risk Management with analyses on price and volume risk, hedge effectiveness, asset optimization and financial performance. It is hybrid, with 3 days per week in the New York City office.

Key responsibilities

• Model an energy portfolio of retail power sales contracts, power and gas hedges, renewable asset PPAs, BESS, heat rate call options and environmental products, ensuring positions are captured and reconciled in valuation and risk measurement.
• Build and maintain stochastic simulation frameworks, calibrate correlated risk factor models and model forward curves and spot market dynamics.
• Analyze retail customer composition, consumption patterns and load characteristics, and forecast their evolution.
• Help identify optimal hedge strategies, evaluate hedge performance and analyze hedge costs and residual exposures.
• Support risk metrics such as Gross Margin at Risk, Value at Risk and Expected Shortfall, and develop tools showing exposure and risk concentration.
• Support structuring activities, evaluate profitability and risk of proposed customer transactions, and analyze regulatory and market structure changes.
• Document and validate models, improve data quality, build automated reporting tools and keep models transparent and audit-ready.

Required profile

• Bachelor's degree in Quantitative Finance, Engineering, Mathematics, Statistics, Computer Science, Data Science or a related quantitative discipline; Master's preferred.
• 3-7+ years of experience in US energy or commodity markets, including quantitative risk analytics, structuring and portfolio management; electricity market experience preferred.
• Familiarity with ISO markets such as PJM, NYISO, ISO-NE or ERCOT preferred.

Required skills

• Quantitative finance, risk analytics, time series forecasting, regression modeling, machine learning and Monte Carlo simulation.
• Proficiency in R, Python, SQL and Excel.
• Knowledge of forward and spot power markets, load-following sales, energy hedging, RECs and carbon products, load forecasting, capacity markets and ancillary services.