Director, Quantitative Analysis

ConocoPhillips•Houston, TX

About The Position

We are seeking a senior quantitative professional to serve as the Lead Quantitative Analyst and working supervisor for ConocoPhillips' commercial quant function. This is a high-impact, player-coach role — you will be a hands-on quantitative contributor as much as a team leader, personally driving model development and owning the mathematical processes alongside the analysts you lead. The role carries responsibility across all COP traded commodities — crude oil, natural gas, power, LNG, NGLs, and risk quantification — and serves as the primary technical authority for model development, valuation frameworks, and the shared quant library that underpins commercial analytics across the organization. You will set the modeling standards, define the quant roadmap, and review the work of quantitative analysts. We are looking for someone who leads by example on the quantitative front, remains deeply engaged in model design and mathematical rigor, and can step in anywhere across quantitative analytics.

Requirements

15+ years of hands-on experience as a quantitative analyst or quant researcher in energy commodities or financial markets, with a demonstrable track record of building production-grade pricing and risk models.
Deep expertise across multiple commodity classes — prior experience spanning at least two of: crude, natural gas, power, LNG, or NGLs is required; coverage across all five is strongly preferred.
Expert-level proficiency in stochastic process modeling, derivatives pricing (Black-76, Monte Carlo, LSMC, spread options), volatility surface calibration, and real options valuation.
Advanced programming skills in Python (required); proficiency in C++ or C# is a strong plus. Must be capable of translating rigorous mathematical frameworks into well-structured, maintainable code — not just one-off analytical scripts.
Strong understanding of energy market structure, physical and financial commodity contracts, and the embedded optionality in storage, transportation, and long-term offtake agreements.
Prior experience in a player-coach or technical lead capacity — managing or mentoring analysts while maintaining active, hands-on model development.
Exceptional mathematical foundation: stochastic calculus, numerical methods, linear algebra, and optimization.
Master’s degree (required) in Mathematics, Physics, Financial Engineering, Computer Science, Engineering, or a closely related quantitative field. PhD strongly preferred.

Nice To Haves

Familiarity with VaR/CVaR model design, Greeks-based hedging frameworks, and model risk governance standards.
Experience integrating quant models with enterprise data platforms (Snowflake, Azure Databricks) and deploying model outputs via REST APIs or interactive dashboards (Power BI, Dash, Streamlit).
Knowledge of optimization methods (LP, MILP, Stochastic Optimization, ADP) as applied to portfolio scheduling, capacity allocation, or dispatch modeling.
Experience working directly on a commodity trading floor or in close partnership with trading desks in a deal-support capacity.
FRM, CFA, or equivalent professional designation is a plus.

Responsibilities

Own the mathematical design and intellectual core of ConocoPhillips' multi-commodity quantitative library — a centralized framework covering valuation, pricing, simulation, optimization, and risk across crude, natural gas, LNG, power, and NGLs.
Design and maintain pricing models for vanilla and exotic derivatives, physical embedded optionality (e.g., storage, transport, swing, tolling, SPAs with flex provisions), and structured commodity products.
Develop and own stochastic price process models — including mean-reverting, GBM, jump-diffusion, and multi-factor models — calibrated to forward curves, volatility surfaces, and historical correlations.
Own option pricing frameworks (Black-76, spread options, real options, Monte Carlo, Longstaff-Schwartz, Least-Squares Monte Carlo) with rigorous delta/Greeks computation and hedging analytics for use by traders, originators, and risk managers.
Build and maintain forward curve construction and calibration routines using spline, bootstrap, and parametric methods, integrating live market data from ICE, Platts, Bloomberg, and internal sources.
Lead model validation efforts including back-testing, stress testing, sensitivity analysis.
Design scalable simulation frameworks suitable for large-scale Monte Carlo workloads, scenario analysis, and optimization loops across multi-commodity portfolios.
Own the mathematical framework and ongoing evolution of a centralized, reusable quantitative library — ensuring models are rigorously documented, peer-reviewed, and grounded in sound, defensible methodology.
Establish and enforce modeling standards, validation protocols, and governance practices across the quant library — setting the bar for mathematical rigor, assumption transparency, and auditability.
Partner with Commercial IT to ensure quant model outputs are accessible and deployable within commercial workflows, while retaining full ownership of the underlying mathematics and model logic.
Ensure all models carry full audit trails, documented assumptions, version-controlled runs, and calculation methodologies defensible for senior leadership and risk governance review.
Lead and mentor a team of quantitative analysts across commodity lines — conducting code reviews, model reviews, and technical coaching to elevate team capability and output quality.
Define and steward the quant team's analytical roadmap in close coordination with commercial leadership, traders, market analysts, and risk management.
Allocate team resources across competing priorities — balancing long-term library development with time-sensitive deal support and ad hoc analytical requests from desks.
Recruit and develop quant talent; establish clear performance standards, technical benchmarks, and career development frameworks for the team.
Serve as the primary interface between the quant team and commercial stakeholders — translating complex model outputs into decision-ready insights for traders, originators, and senior leadership.
Provide direct quant support on high-value commercial transactions — including deal valuation, structuring analysis, bid/offer support, and scenario analysis.
Partner with originators, traders, market analysts and risk managers to ensure quant models are calibrated to current market conditions and reflect deal-specific terms with precision.
Support portfolio-level analysis including value attribution, extrinsic value capture opportunities, hedge effectiveness testing, and optimal hedging strategy design.