About the Role

We are building Alliance Financial OS (AFOS) — a financial operating system for a multi-entity home services platform.

This is not a traditional FP&A role.

You will work at the intersection of:

• Finance
• Data
• AI
• Operations

Your mission is to help turn our financial reporting into a real-time, decision-making system.

What You’ll Do

1. FP&A & Reporting

• Build and maintain monthly FP&A reports (Revenue, EBITDA, variance)
• Analyze Actual vs Budget vs Forecast
• Prepare executive summaries with clear insights (not just numbers)

2. Data & Metrics Layer

• Work with data from:
  • Alliance Financial OS (FOS)
  • CRM (Apollo)
  • Accounting systems (QuickBooks / Sage)
• Clean, structure, and validate datasets
• Define and maintain single source of truth metrics

3. AI-Driven Analysis

• Use tools like Claude, ChatGPT, and other AI tools daily
• Automate:
  • variance explanations
  • reporting summaries
  • data transformations
• Help design AI workflows to reduce manual reporting

4. Dashboards & Visualization

• Build dashboards for:
  • FP&A (Revenue, EBITDA, margins)
  • Unit economics (per job, per technician)
  • Operational performance
• Tools may include:
  • Metabase / Looker / Power BI / Superset

5. Automation & Systems Thinking

• Identify manual processes and replace them with automated workflows
• Continuously improve how financial data flows through the company

What We’re Looking For

Must-Have

• 0–3 years experience (or strong internship experience)
• Strong Excel / Google Sheets skills
• Basic to intermediate SQL
• Strong analytical thinking

AI Requirement (Critical)

You must already:

• Use ChatGPT / Claude regularly
• Understand prompting
• Be able to show examples of using AI to:
  • analyze data
  • automate tasks
  • generate insights

Nice to Have

• Python (pandas)
• Experience with BI tools
• Familiarity with APIs or data pipelines
• Background in finance, economics, or engineering

How You Think

• You are not just a reporter — you are a builder
• You ask: “Why did this change?” and “What should we do next?”
• You are comfortable working with incomplete data and improving it
• You think in systems, not spreadsheets