We are looking for a Deployment Strategist with 2+ years of experience to own workstreams within large-scale AI implementations for health systems. You'll join a fast-growing team building healthcare's financial operating system, working directly with hospital CFOs and finance leaders to drive margin improvements through novel AI solutions.

What will you be doing?

Own specific delivery workstreams within large health system deployments alongside a Senior Deployment Strategist
Lead discovery, solutioning, and ad hoc problem-solving for novel AI products where customers are still defining use cases
Manage working-level relationships with clinical leads, IT project managers, and department heads at health system clients
Move fluidly between client-facing work (stakeholder prep, presentations) and hands-on technical work (AI agent configuration, light code)
Drive operational change and performance improvement for dedicated large-scale accounts (often 12-18 month engagements)

Key Requirements

2+ years at a healthcare consulting firm (e.g., Chartis, ECG, Kaufman Hall, Huron) or health-tech company selling to health systems (e.g., Abridge, Cedar, Open Evidence)
Demonstrated passion for healthcare provider organizations — clear pattern of healthcare focus in career choices
Client-facing experience working directly with health system stakeholders
Technically literate: comfortable with AI/data tools; prior coding experience a strong plus
Based in or willing to relocate to New York City (4 days/week in-office)

You will sit at the intersection of customer healthcare finance problems and the AI that solves them. Analytics Engineers get the data in accurately and encode customer business logic. This role builds on top of that: the context layer agents operate on, tailored agents and predictive models for each customer, and the iteration that turns outputs into decisions and behavioral change. You will work in Python with the company's agent harnesses and eval tooling, partnered with the value team and directly with customers.

You'll feel at home here if you were called a data scientist three to five years ago and have been shipping applied AI since.

What You'll Do:

Build tailored agents. Stand up customer-specific agents and ML models on existing agent infrastructure.
Own the context layer. Maintain the customer context agents operate on, including platform-level agents.
Validate outputs. Use harnesses and evals to keep agents and models accurate enough to earn trust.
Drive decisions. Iterate on insights and presentation with the value team until customers act on them.
Work with customers. Scope problems with end users and present solutions to finance and ops leaders.

Deployment Strategist

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