Strategy and Finance Analyst

Cortexa is building vertical AI for healthcare. We are the AI-native operating partner for independent health practices: we take over the entire non-clinical operation of a practice, billing, front office, payroll, accounting, and day-to-day management, and run it with a system of intelligence built from the operational context that today is trapped in people's heads and scattered across disconnected tools. Clinicians operate the practice. Cortexa operates the business.

We start in behavioral health, where our founder has operated, and the same operating partner extends across the long tail of independent practices that enterprise healthcare software will never serve: physical therapy, dentistry, veterinary, and beyond.

The founding team previously built Sixer and sold it to Dream11, one of the largest gaming companies in Asia, and is building Cortexa together as that same team. Our co-founder also built Gaya Therapy, a multi-state behavioral health group he ran by hand before automating it, and which is now Cortexa's first customer. We have the domain insight to know what to build and the engineering depth to build it. The team is deliberately small and the work is done AI-first.

The Role

Before Cortexa can automate how a practice runs, someone has to figure out how it should run. That is this seat. You go deep on the practices we operate, work out the best way to run each part of the business, prove it works, and hand the team a playbook clear enough to encode into the system of intelligence. It is a strategy and operations role first, with the financial rigor to back the recommendations.

You report directly to the co-founders. There is no analyst pool and no layer between you and the decisions. The work you do goes to founders, the practices we operate, and investors. It spans three tracks.

1. Build the operating playbook (the IP)

The reusable frameworks for how each part of a practice's operation should run, the playbook Cortexa applies across practices and eventually encodes into the system of intelligence:

• How the operation works end to end: intake and front office, billing, payroll, scheduling and capacity, day-to-day management, and where each one breaks
• The economics underneath it: revenue per session, payer and pay-mix, clinician utilization, contribution margin per provider
• Compensation and pricing logic: how clinicians are paid, and how Cortexa prices and staffs the operation it runs as work shifts from people to AI
• Operating budget and forecasting templates for the practices we run

2. Apply it on the practices we operate

• Go deep on a specific practice: map how it actually runs, find what is inefficient or leaking, and redesign it
• Use EHR data and Cortexa's own scoring to diagnose where revenue, time, and margin are lost
• Turn each fix into something repeatable: a process the team can run today and engineering can automate tomorrow
• Work directly with owners and sit in on calls to see how the playbook lands and where it needs sharpening

3. Sharpen Cortexa's own strategy

• Market sizing and segmentation across the verticals we expand into
• Pricing, take-rate, and the economics of the operating-partner model itself
• Partnership economics (referral, billing, and white-glove partners)
• The strategy memos and decks that go to the founders, the board, and investors

The throughline across all three: you figure out the best way to run an operation, prove it on a real practice, and hand engineering a codified playbook to automate. You do this AI-natively, using Claude Code and agentic workflows to research, model, draft, and analyze, and you build durable workflows (prompts, subagents, MCP integrations, scheduled agents) the rest of the team inherits. The frameworks you build are not just analysis; they are early drafts of the intelligence that will one day run the operation.

What We're Looking For

AI fluency (the primary filter)

• You already use tools like Claude, ChatGPT, or Cursor daily, and you have a considered view of what each is good for
• You are hands-on with at least one agentic environment (Claude Code, Cursor, Windsurf, Cline). Experience building MCP integrations or subagent workflows is a plus
• You can take a brief, break it into steps AI can execute, and ship a result a founder would use without rework
• You treat AI as something you delegate to and review, not a search bar, and you can diagnose a weak output the way you would coach a junior analyst
• You are comfortable with the terminal, git, and Markdown. Python or SQL helps, but most gaps there are ones AI can close

Strategy and analytical fundamentals

• A recent or upcoming degree in a quantitative or analytical field (Economics, Strategy, Business, Finance, Engineering, or similar), ideally with 0 to 3 years in consulting, an operating role at a startup, or finance
• You can take a messy real-world operation, break it into its parts, find what is broken, and decide what to fix first
• Comfortable with numbers and models: you can reason about unit economics, build a clean model, and defend the logic in a meeting
• The ability to turn a vague founder ask into a structured framework without hand-holding
• Clear writing: you can compress a complex situation into a one-page memo a busy reader will actually finish

Working style

• Self-directed on a lean team. No one will review your work hour to hour, so you set the bar
• Comfortable owning output that reaches founders, clients, and investors

Bonus

• Operations, process-improvement, or consulting reps where you have actually changed how something runs
• Financial or accounting depth (modeling, close cycles, tax exposure), since you will reason about practice books and structures
• SQL or BI exposure (Metabase, Looker, BigQuery)
• A personal or family connection to healthcare, behavioral health, or small-business ownership
• Something you have shipped (a model, a case competition, a treasury, an AI workflow) that you can show us

Why This Seat

• You work on the strategy and operations core of an AI-native company, not a slide in a deck
• A direct line to the founders and to the decisions that move the business
• Real ownership of the frameworks Cortexa runs on, early enough that they become the company's IP
• A chance to get genuinely good at AI-native analytical work, alongside a team that has built and exited before