Quantitative Economic Modeler - Contractor

Join Jupiter's Product organization to help build the risk engine behind our economic modeling. Working directly with the Principal PM, for Economic & Financial Modeling, you will translate climate hazard data into actionable financial metrics like EBITDA impact, CapEx/OpEx, and credit risk.

This is an associate-level, development-focused contract role designed for someone with solid Python and data engineering skills alongside a foundation in economics or finance. You will spend your time building and scaling model infrastructure while expanding your financial domain knowledge on the job through direct mentorship.

What You Will Do

• Build & Scale Pipelines: Write modular, well-tested Python code to move models from research prototypes into production pipelines across large company and asset datasets.
• Support Model Research: Assist in prototyping statistical, econometric, and machine learning models (e.g., time-series analysis, regression) to project climate hazard impacts.
• Data Engineering: Source, clean, and transform diverse financial, building, and hazard datasets, resolving missing data or anomalies.
• Cross-Functional Collaboration: Partner with engineering, product, and client teams to document outputs and communicate analytical findings clearly.

What You Will Bring

• Degree in a quantitative field (Computer Science, Finance, Economics, Statistics, or related discipline) or equivalent practical experience.
• Effective Python coding skills with a focus on writing clean, production-quality code (rather than one-off scripts).
• Foundational understanding of finance or economics and an eagerness to learn more.
• Basics of data engineering (sourcing, transforming, cleaning data) and standard git workflows.
• Ability to work both independently as well as collaboratively with multiple teams.

What Sets You Apart

• Experience working with large datasets, Docker, cloud environments (AWS/GCP/Azure), or tools like pandas/Polars.
• Coursework, projects, or practical exposure in financial modeling, credit risk, or physical climate risk.
• Coursework or experience in financial engineering, credit risk, or quantitative modeling.