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.
Skills Required
- Degree in Computer Science, Finance, Economics, Statistics, another quantitative field, or equivalent practical experience
- Effective Python coding skills focused on clean, production-quality code
- Foundational understanding of finance or economics
- Basic data engineering skills, including sourcing, transforming, and cleaning data
- Experience with standard Git workflows
- Ability to work independently and collaboratively with multiple teams
- Experience working with large datasets
- Experience with Docker
- Experience with cloud environments such as AWS, GCP, or Azure
- Experience with pandas or 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
What We Do
Jupiter is the global leader in data and analytics services to make informed decisions to anticipate risk from extreme weather, sea-level rise, storm intensification and rising temperatures caused by short, medium and long-term climate change. Jupiter’s ClimateScore TM Intelligence Platform provides sophisticated, dynamic, hyper-local, current- hour-to-50-plus-year probabilistic risk analysis for weather in a changing climate. The company’s FloodScore™, HeatScore™, WindScore™, FireScore™, and ClimateScore Global™ services are used for climate-related risk assessment and management worldwide.
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