Quanititive Associate, Capital Markets

Palmetto Clean Technology•New York, NY

22h•Onsite

About The Position

The Quantitive Associate, Capital Markets sits at the center of Palmetto Capital's corporate strategy and structured finance team, turning Palmetto's data backlog into the insights that shape capital decisions. The primary focus is strategic and analytical: mining internal and external data to inform financing and capital structure decisions, and translating existing Excel-based models into Python to make analysis faster, more scalable, and more repeatable. This person also supports sourcing of new capital opportunities and contributes to select modeling and transaction execution work, with a sharp eye for detail and a comfort moving between code, data, and financial analysis.

Requirements

Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, Engineering, or a related quantitative field; coursework or hands-on exposure to finance, economics, or accounting required
2-4+ years of experience in a data analytics, data science, quantitative research, or software engineering role, with meaningful exposure to finance, fintech, or capital markets
Strong proficiency in Python (pandas, numpy, or similar) and SQL; experience translating Excel-based financial models into code strongly preferred
Experience building models, dashboards, or automated reporting and analysis tools from large, complex datasets
Strong analytical and quantitative skills, with close attention to detail
Ability to manage multiple workstreams simultaneously in a fast-paced environment
Excellent communication skills, with the ability to translate technical analysis for non-technical, senior audiences
Highly motivated, collaborative, and comfortable operating with some ambiguity

Nice To Haves

Working knowledge of financial modeling concepts (debt, equity, tax equity, ABS, or similar structures) a plus

Responsibilities

Mine Palmetto's data backlog — portfolio, financing, operational, and market data — to surface insights that inform capital structure and financing decisions
Build Python-based analytical tools, models, and dashboards that translate large, complex datasets into clear, decision-ready outputs for senior leadership
Translate existing Excel-based structured finance models into Python to improve speed, scalability, and repeatability of analysis
Analyze market, competitor, and macroeconomic trends, using code-driven analysis to inform financing and capital structure decisions
Contribute to board-level and executive materials that connect data-driven analysis to Palmetto's broader growth strategy
Assess the company's future capital position and financing needs, incorporating relevant legislative and regulatory data
Automate recurring financial and operational reporting for key stakeholders, partnering with technology teams to reduce manual effort
Drive pricing analysis across financing structures and capital products using quantitative, data-driven methods
Support sourcing and evaluation of new capital opportunities and financing products through data-driven structuring and financial analysis
Build exploratory Python models to size and stress-test capital structures and financing products that haven't been executed in the market, working in parallel with the team to refine and automate assumptions
Run scenario and sensitivity analyses, using code-based tools, to pressure-test new structures and identify the optimal capital mix
Support meetings with prospective investors by preparing data and analysis on new capital opportunities and financing facilities
Support execution of select debt, tax equity, or structured finance transactions, working with banks, investors, and legal counsel as needed
Maintain and query deal-tracking data across live and prospective financings
Support due diligence and term sheet review with data pulls and analysis as needed
Partner closely with FP&A, Treasury, and Accounting
Identify and implement improvements to data infrastructure, tooling, and modeling processes
Stay attuned to the needs of internal stakeholders and capital partners, anticipating data and analytical needs