Note: The job is a remote job and is open to candidates in USA. Jupiter Intelligence provides analytics for resilience planning and enterprise climate risk management. The Quantitative Economic Modeler will build and scale Python-based model infrastructure, support statistical, econometric, and machine learning research, and translate climate hazard data into financial metrics. The role also involves data engineering and collaboration with engineering, product, and client teams.
Responsibilities
- Write modular, well-tested Python code to move models from research prototypes into production pipelines across large company and asset datasets
- Assist in prototyping statistical, econometric, and machine learning models (e.g., time-series analysis, regression) to project climate hazard impacts
- Source, clean, and transform diverse financial, building, and hazard datasets, resolving missing data or anomalies
- Partner with engineering, product, and client teams to document outputs and communicate analytical findings clearly
Skills
- 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
- Successful candidates must be authorized to work in the US
- 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
Benefits
- Remote work arrangement
- Direct mentorship to expand financial domain knowledge on the job
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