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Clover Health

Director, AI-Enabled Fraud Prevention & Investigations

Work from home

Remote role where the employee must remain based in a particular country.

United States only

Employer listed it 9 days ago · Added 4 days ago

First listed 9 days ago and still open.

Salary

$150k–$240k a year

Location

United States only

Timezone

US East

Contract

Full-time

Experience

Lead

Category

Finance

Stated by the employer in the job description

Remote flexibility

Work from home

This is a remote role, but the employee must be based in United States. It is work from home rather than work from anywhere.

What the employer says

  • Source listing states candidate location: "Remote - USA"

What Nomaders makes of it

  • Residency required in United States
  • Payroll and tax are likely handled in that country only

The quotes above are the employer's own words; the reading is ours. Always check the original listing and employment terms before working from another country.

About the role

Clover Health is investing in our fraud detection and investigation capabilities and is seeking a Director, AI-Enabled Fraud Prevention & Investigations to join our Legal department as a senior individual contributor. This hybrid role combines: (1) managing hands-on investigations of potential provider fraud with (2) building AI-powered tools and processes to make investigations more efficient and help catch potential future fraud earlier. You will develop and pilot new AI-enabled investigatory processes for Clover’s Medicare Advantage business, and own resulting investigations, in collaboration with Clover’s Special Investigations Unit (SIU) and other stakeholders.

This is a non-attorney role. You will work hand-in-hand with Clover’s lawyers and SIU to ensure that new investigatory processes you develop, and the resulting investigations, comply with Clover policies and regulatory requirements. Success in this role requires someone sharp, creative, and technically hands-on—someone who does not just use existing analytics tools but builds their own. That might mean writing a script to automate repetitive work, pulling apart a dataset when a standard report falls short, or working with an LLM until it produces something genuinely useful—all with the goal of finding smarter, more effective ways to investigate potential provider fraud.

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