Quantitative Portfolio Manager

SmartestEnergy•New York, NY

22h•Hybrid

About The Position

The Quantitative Portfolio Manager develops, maintains, enhances, and uses quantitative models and tools to estimate and manage risks in the company’s energy portfolio. The role supports Trading and Risk Management with analysis of price and volume risk, hedge effectiveness, asset optimization, and financial performance. It combines quantitative, statistical, and programming expertise with energy-market knowledge to improve portfolio risk measurement and inform strategic decisions. The work focuses on modeling forward and spot prices, retail load, and structured transactions to quantify risk under different market conditions and evaluate hedge strategies. This is a hybrid position requiring three days per week in the company’s New York City office. The portfolio includes retail power sales contracts, power and gas hedges, renewable asset PPAs, battery energy storage systems (BESS), heat rate call options, and environmental products. The role also supports transaction structuring, market and commercial analysis, model governance, and the development of scalable analytical tools and reporting.

Requirements

Bachelor’s degree in quantitative finance, engineering, mathematics, statistics, computer science, data science, or a related quantitative discipline.
3–7+ years of experience in US energy or commodity markets in quantitative risk analytics, structuring, or portfolio management.
Experience with portfolio risk measurement and calculation techniques.
Proficiency with R or Python, SQL, and Excel.
Knowledge of quantitative finance, risk analytics, time-series forecasting, regression modeling, machine-learning techniques, Monte Carlo simulation, optimization methodologies, and forecasting methodologies.
Understanding of forward and spot power markets, load-following power sales, energy hedging strategies, environmental products such as RECs and carbon products, load forecasting methodologies, capacity markets, and ancillary services.
Strong analytical and problem-solving skills; ability to translate complex quantitative findings into business recommendations; attention to detail and intellectual curiosity; commercial mindset; effective communication and presentation skills; and ability to work independently and manage competing priorities.

Nice To Haves

Master’s degree preferred.
Experience in electricity markets preferred.
Familiarity with ISO markets such as PJM, NYISO, ISO-NE, or ERCOT preferred.
Experience with statistical modeling packages.

Responsibilities

Model the dynamics of the energy portfolio, including retail power sales contracts, power and gas hedges, renewable asset PPAs, BESS, heat rate call options, and environmental products.
Ensure portfolio positions are accurately captured, reconciled, and represented in valuation and risk measurement processes.
Develop and maintain stochastic simulation frameworks to quantify portfolio risk and expected financial outcomes.
Calibrate correlated risk-factor models to capture dependencies across portfolio exposures.
Model forward-curve evolution and spot-market dynamics using fundamental and market-driven factors.
Analyze and forecast changes in retail customer composition, consumption patterns, and load characteristics.
Assist in identifying optimal hedge strategies that balance risk and profitability.
Evaluate hedge performance and effectiveness against portfolio objectives, and analyze hedge costs and residual exposures.
Support calculation of risk metrics, including Gross Margin at Risk (GMaR), Value at Risk (VaR), and Expected Shortfall (ES).
Evaluate portfolio sensitivities and tail-risk exposures under market and operational scenarios.
Develop tools that provide visibility into portfolio exposures and risk concentrations.
Support structuring activities through analytical modeling.
Evaluate profitability and risk characteristics of proposed customer transactions and recommend portfolio composition and risk mitigation opportunities.
Analyze regulatory and market-structure changes affecting portfolio performance.
Document, validate, and govern models; improve data quality and analytics processes; develop scalable analytical tools and automated reporting; and maintain model transparency and audit readiness.