Associate Director – Infrastructure (Project Finance), Denver

S&P Global•Englewood, CO

5d•Hybrid

About The Position

Grade Level (for internal use): 12 The Team Credit Ratings is part of S&P Global's Global Ratings Services, bringing together more than 1,400 analysts across leading financial centers worldwide. The Infrastructure & Project Finance practice delivers credit analysis on essential, long-lived assets and ring-fenced project financings, covering contractual frameworks, demand and operating risks, sponsor strength, and financial resilience under stress. The North America Infrastructure Ratings team provides independent credit analysis and ratings on essential infrastructure assets and financings throughout the U.S. and Canada — spanning transportation, energy and utilities, digital infrastructure (including data centers), and social infrastructure — across both project finance structures (PPP/availability-based, contracted-revenue, and demand-risk) and corporate debt, including regulated Investor-Owned Utilities, midstream operators, LNG developers, and power generators. Supported by a strong pipeline of new issuances and refinancings, the team deliver s time ly , rigorous credit assessment, clear rating rationale, and ongoing surveillance, supporting capital formation and market understanding across rapidly evolving subsectors.

Requirements

5+ years of relevant experience in project finance or infrastructure finance, including roles in ratings, banking, advisory, investing, or credit.
Demonstrated understanding of infrastructure project structures and documentation, including PPP agreements, EPC and O&M contracts, and offtake or availability-payment frameworks.
Demonstrated financial analysis skills, including cash flow modeling, ratio and covenant analysis, and accounting fundamentals.
Demonstrated ability to deliver high-quality credit analysis across a portfolio with limited oversight and coach junior analysts through structured feedback and quality review.
Practical experience embedding generative AI, large language models, or agentic workflows into analytical processes with disciplined governance, validation, and responsible AI practices including bias awareness and adherence to approved boundaries.
Effective written and verbal communication skills for committee-ready documentation and engagement with issuers, investors, and market participants.

Nice To Haves

Bachelor's degree or equivalent required. A master's degree or professional certification (such as CFA) is highly valued.
We welcome candidates from traditional finance disciplines as well as diverse academic backgrounds that support innovative and critical thinking.
Experience scaling automation or agentic workflows by defining requirements, supporting testing, and enabling team adoption with appropriate governance controls.
Experience simplifying analytical processes through standardization, tooling, and automation.
Demonstrated credit analysis skills with the ability to connect market and sector developments to emerging credit risk themes.

Responsibilities

Coach junior team members on credit fundamentals, modeling discipline, and the responsible use of AI tools, coordinating work across credits to manage quality and capacity risks.
Embed continuous improvement into team routines by developing analytical playbooks, prompt libraries, and standard processes, and driving their adoption across the team.
Lead end-to-end credit analysis for an assigned portfolio of North America infrastructure project finance credits, preparing committee-ready materials and participating actively in rating committees.
Manage portfolio surveillance and updates, connecting market developments and project performance to forward-looking risk themes, and embed AI tools into analytical workflows with disciplined validation and documentation.
Synthesize data, analysis, and research into clear, market-facing narratives for senior stakeholders and market participants, and contribute to thought leadership and sector commentaries.
Ensure adherence to analytical methodology, confidentiality standards, and data governance requirements, escalating risks early and contributing to controlled automation workflows with appropriate governance and human oversight.