zocdoc

AI Support Operations Manager

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At a Glance

Location
New York, United States
Experience
5–7 years
Posted
2026-04-02T17:12:53-04:00

Key Requirements

Required Skills

CRM

Domain Knowledge

  • Automation
  • Engineering

Benefits & Perks

Time Off

Soho location Unlimited Vacation 100% paid employee health ben

Requirements

5–7 years of experience, including 3–5 years in customer support operations, CX tooling, or similar roles

Hands-on experience implementing and optimizing AI-powered support tools (e.g., Forethought AI) and working within CRM platforms

Experience translating support processes and SOPs into scalable, system-driven workflows and automations

Experience partnering cross-functionally with Product, Engineering, CX, and Analytics to deliver operational solutions

Experience managing external vendors or AI platform partners and influencing product direction

A strong sense of ownership and bias to action, with a passion for applying AI and automation to improve customer and agent experiences

Compensation & Benefits

Flexible, hybrid work environment at our convenient Soho location

Unlimited Vacation

100% paid employee health benefit options (including medical, dental, and vision)

Commuter Benefits

401(k) with employer funded match

Corporate wellness program with Wellhub

Responsibilities

As an AI Support Operations Manager, you’ll play a meaningful role in evolving how we deliver customer support by using AI to create more efficient, high-quality experiences.

This is a first-of-its-kind role at Zocdoc, created at a pivotal moment in our growth as we invest in smarter support operations.

You’ll own how AI is designed and scaled across chat, voice, and email, helping automate repetitive work so our teams can focus on complex, human-centered interactions.

You’ll partner across teams to turn emerging AI capabilities into real, measurable outcomes.

Personally motivated by improving customer experiences through innovation and thoughtful application of AI

You genuinely love solving complex operational problems and turning ambiguity into scalable solutions