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Airbnb

Senior Risk Tools & Automation Lead

Remote, India; IndiaRemote · India
Staff · Community Support
POSTED 3 NOVVERIFIED 5H AGO
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Pay · posted
₹37.8L–54L
H-1B median $204k
Open for
0d
since 3 Nov
H-1B · FY26
164
NL sponsor · UK sponsor
Hiring · 30d
212
0 net · open roles

Airbnb was born in 2007 when two hosts welcomed three guests to their San Francisco home, and has since grown to over 5 million hosts who have welcomed over 2 billion guest arrivals in almost every country across the globe. Every day, hosts offer unique stays and experiences that make it possible for guests to connect with communities in a more authentic way.

About the Company

Airbnb was born in 2007 when two hosts welcomed three guests to their San Francisco home, and has since grown to millions of hosts who have welcomed guests in almost every country across the globe. Every day, hosts offer unique stays and experiences that make it possible for guests to connect with communities in a more authentic way.

The Community You Will Join

The Payments Risk Operations team protects Airbnb's guests, hosts, and the platform from fraud, financial loss, and abuse. Our investigators and agents review high-risk activity every day, and the quality of their tools directly shapes the speed, accuracy, and consistency of every decision they make. You will join a team of operators, analysts, and program leaders who partner closely with Platform, Engineering, Data Science, Design, and Legal to keep our community safe.

The Difference You Will Make

As the Senior Risk Tools & Automation Lead, you will own the end-to-end agent experience for risk and fraud operations. You will define what our investigators need, design and conceptualize the next generation of the risk workbench, and drive automation through process efficiencies and AI. You will be the voice of the agent in tooling decisions and the bridge between operational reality and technical execution. Your work will reduce manual effort, shorten handle times, improve decision quality, and free our experts to focus on the most complex cases.

A Typical Day

Agent experience

  • Own the end-to-end agent experience across risk and fraud investigation workflows, from case intake through decision, documentation, and quality review.
  • Spend time with agents to observe workflows, map pain points, and quantify friction in handle time, clicks, context switching, and error rates.
  • Establish feedback loops (surveys, office hours, champion networks, ticket intake) so agent needs are captured, prioritized, and closed out transparently.
  • Partner with Training, Quality, and Workforce Management to ensure tooling changes land with clear enablement, documentation, and adoption tracking.

Tooling requirements and improvements

  • Translate operational needs into clear, prioritized business requirements, user stories, and acceptance criteria that Platform and Engineering can build against.
  • Own and manage the operational risk tooling roadmap and backlog, balancing quick wins, foundational investments, and policy or regulatory needs.
  • Lead intake, triage, and prioritization of tooling requests using impact sizing (time saved, loss prevented, accuracy gained, risk reduced).
  • Drive user acceptance testing (UAT), launch readiness, and post-launch measurement for every tooling release.

Workbench design and conceptualization

  • Define the long-term vision for a unified risk workbench that brings signals, account history, payment data, linked entities, and decisioning into a single, intuitive workspace.
  • Partner with Platform, Design, and UX Research to run discovery, build journey maps, create wireframes and prototypes, and conduct usability testing with agents.
  • Champion design principles of simplicity, consistency, and accessibility so agents can make accurate decisions quickly and confidently.

Automation through efficiencies and AI

  • Identify, size, and prioritize automation opportunities across manual and repetitive work, including case routing, data gathering, enrichment, summarization, and low-risk decisioning.
  • Partner with Platform, Engineering, and Data Science to design and deploy AI-assisted capabilities such as case summaries, recommended actions, agent copilots, and automated documentation.
  • Define human-in-the-loop guardrails, quality controls, and monitoring so automation improves efficiency without compromising accuracy, fairness, or compliance.
Source · Airbnb · GreenhouseFirst seen 3 NovOriginal ↗Method