Associate Quantitative Developer - Climate Risk Modeling

Jupiterintel; San Mateo (CA); On-site; USD 90,000 - 120,000; Part time

Job summary

Jupiter Intelligence is seeking an associate-level, contract data engineer to translate climate risk data into financial metrics. You will build scalable Python pipelines and collaborate with engineering, product, and client teams while expanding your finance knowledge.

Responsibilities include moving models from research to production, prototyping statistical and econometric methods, and handling large, diverse datasets in cloud environments using Docker, Pandas, and Polars.

Qualifications

• Degree in a quantitative field or equivalent practical experience.
• Proficient Python coding focused on production-quality code.
• Foundational finance/economics knowledge and eagerness to learn.
• Data engineering basics: sourcing, transforming, cleaning data.
• Ability to work both independently and with multiple teams.
• Experience with large datasets and modern data tools.
• Familiarity with Docker and cloud environments.

Responsibilities

• Build and scale pipelines to move models from research to production.
• Support model research with econometric and ML methods.
• Source, clean, and transform diverse financial, building, and hazard datasets.
• Collaborate with engineering, product, and client teams to document outputs.

Skills

Python programming

Data engineering basics

Finance/Economics knowledge

Independent work

Team collaboration

Large datasets

Cloud environments

Pandas/Polars

Education

Quantitative degree or equivalent

Tools

Docker

AWS

GCP

Azure

Pandas

Polars

Job description

Jupiter Intelligence is seeking an associate-level, contract data engineer to translate climate risk data into financial metrics. You will build scalable Python pipelines and collaborate with engineering, product, and client teams while expanding your finance knowledge.

Responsibilities include moving models from research to production, prototyping statistical and econometric methods, and handling large, diverse datasets in cloud environments using Docker, Pandas, and Polars.