Quantitative Analytics interview prep.

What interviewers look for

• Can the candidate think in fundamentals first - load, weather, fuel stack, renewables, outages - and only then in stochastic processes? A hub price without a fundamentals story is a red flag.
• Do they understand spreads as options - location (basis), time (storage / calendar), quality (heat-rate / crack / dark) - and price the optionality, not just the forward?
• Can they value a physical asset (storage, transport, tolling, peaker, battery) as a strip of options, separating intrinsic from extrinsic value, and explain LSMC at a working level?
• Are they rigorous about market mechanics - hub vs node, capacity vs energy vs ancillary, day-ahead vs real-time, FTR / CRR - not just a single price curve?
• Do they reason in net-of-cost, real-world terms - transmission, transport, storage tariffs, slippage, exercise constraints - not just gross spread?
• Are they intellectually honest about model risk - backtest bias, regime change, calibration instability - and can they articulate where their model breaks?
• Do they own the producer / consumer mindset behind the trade - what the physical flow has to do for the model to be right - or do they treat it as pure math?

Behavioural questions to expect

1. Walk me through your CV.

   What it tests: Story coherence + genuine fit for energy-trading quant. Desks want evidence of fundamentals literacy (a commodity, a market, a physical system) and builder's instinct (research + code), not a pure-finance quant who treats power as just another asset.

2. Tell me about your most rigorous piece of energy / commodity quant work.

   What it tests: Research maturity + fundamentals literacy + intellectual honesty. Tests whether the candidate validated a result (out-of-sample, robustness) and grounded it in a physical fundamentals story, not a pure statistical artifact.

3. Why energy / commodity trading quant - and not pure financial-markets quant or pure power-systems engineering?

   What it tests: Authentic alignment with the fundamentals-meets-statistics seam. Tests whether the candidate is drawn to the physical-meets-financial nature of energy, not just shopping the next quant seat.

4. Why this commodity / market mix - the sector, power vs gas vs LNG vs emissions, US vs European vs Asian?

   What it tests: Specificity. Generic 'energy transition is exciting' answers fail. Tests whether the candidate has actual market-mechanics curiosity.

5. Why this firm?

   What it tests: Real homework - book composition, physical-asset access, modelling stack, team - not name-drop.

6. What's your read on our trading book and modelling stack?

   What it tests: Whether the candidate has internalised HOW this firm makes money - commodity + tenor + asset access + modelling edge - not just WHAT it trades. Probes research-to-deployment pipeline awareness.

7. How do you think this firm manages model risk + market risk?

   What it tests: Whether the candidate understands energy market risk is not just VaR - it's basis blow-up, scarcity tails, weather shocks, asset constraints - and that model risk (calibration, regime change, backtest bias) is a first-class risk.

8. Walk me through how you'd build a hub-price forecast for the sector - power, gas, or LNG - over the next 12-24 months.

   What it tests: Whether the candidate thinks fundamentals-first (load, weather, fuel stack, renewables, outages, capacity) and only then maps to a stochastic process; tests if they can name the supply + demand drivers and link them to merit-order pricing.

Technical concepts to master

Practical drills

• A gas peaker has heat rate 9.0 MMBtu/MWh and VOM $3/MWh. Front-month power forward is $50/MWh, gas $3.50/MMBtu. Power vol 60%, gas vol 40%, correlation 0.6. (a) Compute the intrinsic spark spread per MWh. (b) Compute the spread vol. (c) Roughly estimate the extrinsic value for a 1-month at-the-money option assuming 21 trading days.
• Walk through how you'd value a 10 Bcf gas storage facility with 200 MMcf/day max injection + 300 MMcf/day max withdrawal, currently 50% full, against TTF or the US national gas hub forward curves. 8-10 min delivery, 10-15 min Q&A.
• A candidate shows a model: Sharpe 3.5 over 2019-2023 on a basis-trading strategy in PJM (West hub vs Eastern hubs). Backtested by trying ~500 specifications, run on revised ISO load data, valued at hub-to-hub spread with no transmission cost, no FTR cost. What's wrong, and how would you actually validate it?

Smart-question anchors

• Book + asset access - which commodities + markets the desk trades + which physical assets the quant supports
• Modelling stack + tooling - fundamentals models, price + spread models, LSMC + risk, build vs buy stance
• Research-to-deployment pipeline - how a model goes from research to live capital + who owns model validation
• Risk + model validation - market risk + model risk regime, stress + scenario, kill criteria for a degrading model
• Data edge - alt data, weather data, fundamentals data, vintage-stamped discipline

Sourced from

• Eydeland & Wolyniec - Energy and Power Risk Management
• Burger, Graeber, Schindlmayr - Managing Energy Risk
• Longstaff & Schwartz - Valuing American Options by Simulation
• FERC + ISO / RTO market design + EIA data
• Wall Street Oasis + Global Derivatives community (energy desk threads)
• Bailey & Lopez de Prado - Deflated Sharpe Ratio + backtest overfitting