Director, Quantitative Analysis

ConocoPhillips•Houston, TX

2d•Onsite

About The Position

ConocoPhillips is seeking a senior quantitative professional to serve as the Lead Quantitative Analyst and working supervisor for its commercial quant function. This is a high-impact, player-coach role where the individual will be a hands-on quantitative contributor and team leader, personally driving model development and owning mathematical processes alongside the analysts they lead. The role has responsibility across all ConocoPhillips traded commodities, including crude oil, natural gas, power, LNG, NGLs, and risk quantification. The position serves as the primary technical authority for model development, valuation frameworks, and the shared quant library that supports commercial analytics across the organization. The successful candidate will set modeling standards, define the quant roadmap, and review the work of quantitative analysts, leading by example on the quantitative front, remaining deeply engaged in model design and mathematical rigor, and capable of stepping 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.
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, 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.
Build and maintain forward curve construction and calibration routines using spline, bootstrap, and parametric methods, integrating live market data.
Lead model validation efforts including back-testing, stress testing, and sensitivity analysis.
Design scalable simulation frameworks suitable for large-scale Monte Carlo workloads, scenario analysis, and optimization loops.
Own the mathematical framework and ongoing evolution of a centralized, reusable quantitative library, ensuring models are rigorously documented, peer-reviewed, and grounded in sound methodology.
Establish and enforce modeling standards, validation protocols, and governance practices across the quant library.
Partner with Commercial IT to ensure quant model outputs are accessible and deployable within commercial workflows.
Ensure all models carry full audit trails, documented assumptions, version-controlled runs, and calculation methodologies.
Lead and mentor a team of quantitative analysts, conducting code reviews, model reviews, and technical coaching.
Define and steward the quant team's analytical roadmap in 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.
Recruit and develop quant talent; establish clear performance standards, technical benchmarks, and career development frameworks.
Serve as the primary interface between the quant team and commercial stakeholders, translating complex model outputs into decision-ready insights.
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.
Support portfolio-level analysis including value attribution, hedge effectiveness testing, and optimal hedging strategy design.