Quantitative Analytics Manager

Position Summary

The Quantitative Analytics Manager leads a team of quantitative analysts responsible for advanced analytical tools and market insights that inform trading strategies across the commercial organization. This role is pivotal in enhancing trading performance through statistical modeling, algorithmic strategy development, risk analytics, and real‑time decision support across crude, products, freight, and NG/NGL markets. The team partners closely with fundamental analysts, traders, originators, risk managers, and IT to drive innovation and implement scalable quantitative capabilities that deliver measurable commercial value.

The manager ensures alignment between business objectives, exploratory analytics, and IT‑led production of enterprise‑grade mature models and data products. This role champions a culture of curiosity, experimentation, and evidence‑based decision making.

• Lead and mentor a team of quantitative analysts supporting front‑line commercial and trading teams.
• Translate commercial objectives into clear analytical roadmaps, prioritizing projects based on value, feasibility, and business impact.
• Partner with fundamental analytics, trading, origination, risk, IT and other internal teams to identify opportunities for analytical innovation.
• Provide real‑time analytical support and insight for commercial negotiations, deal structuring, asset optimization, and risk management.
• Champion adoption of new analytical technologies, frameworks, and tools.
• Design and validate statistical, econometric, and machine learning models to enhance market understanding and commercial performance.
• Ensure rigorous model governance, documentation, version control, and performance monitoring.

• Bachelor’s degree in a quantitative field (e.g., Applied Mathematics, Statistics, Engineering, Quantitative Finance, etc.) required; advanced degree preferred.
• Eight (8) years of experience in trading analytics, quantitative research, or commercial decision support in energy or commodity markets.

• Proficiency in Python, SQL, and modern data‑science toolkits; experience with cloud environments (AWS, Azure, or GCP).
• Deep understanding of energy commodity market fundamentals.
• Experience with derivative pricing, stochastic modeling, time‑series forecasting, or optimization techniques (e.g., linear/mixed‑integer).
• Strong communication skills and ability to translate complex statistics into business outcomes.
• Curiosity, creativity, and continuous improvement mindset.

Houston, TX (Relocation available;
Estimated travel