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This is a remote position.
Junior (Corporate) Finance Analyst – Remote Job, 1+ Year Experience
Annual Income: $65K – $70K
A valid work permit is necessary in the US.
About us: Patterned Learning is a platform that aims to help developers code faster and more efficiently. It offers features such as collaborative coding, real-time multiplayer editing, and the ability to build, test, and deploy directly from the browser. The platform also provides tightly integrated code generation, editing, and output capabilities.
Accountabilities
- Build and optimize financial models for new Renewable technologies and markets.
- Track, update, and maintain project-level budgets and reporting
- Conduct quantitative feasibility analysis on new markets, participants, projects, and trends
- Conduct risk forecast analysis on existing portfolio and new assets/markets
- Assist in running corporate financial models, monitor markets, and produce Monthly / Quarterly Reports for existing investment portfolios
- Collaborate with Finance team on building new Investment Business Cases
- Support the capital markets team in due diligence, deal execution, and investment documentation
Qualifications:
- Bachelor’s degree in Finance or Accounting or related field
- Excellent quantitative analysis skills
- Excellent financial analysis and knowledge of Excel
- Excellent presentation and communication skills
- Be capable of analyzing huge amounts of financial and numerical data and extracting, summarizing, and communicating key results
- Work pro-actively individually, and collaboratively within the team
- Be highly organized, follow procedures, and meet timelines
- Preference for 1 year of relevant solar project finance experience
- Preference for experience at a utility, fund, or investment bank
Why Patterned Learning LLC?
Patterned Learning can provide intelligent suggestions, automate repetitive tasks, and assist developers in writing code more effectively. This can help reduce coding errors, improve productivity, and accelerate development.
Pattern recognition is particularly relevant in the context of coding. Neural networks, intense learning models, are commonly employed for pattern detection and classification tasks. These models simulate human decision-making and can identify patterns in data, making them well-suited for tasks like code analysis and generation.
