Tempus AI

Tempus AI

Senior Vice President, Medical Affairs

Remote - New York · Exec

Sponsorship not specified$365k-$475kDetected 8 days ago
Clinical TrialsClinical ResearchPatient CareResearchExperimental DesignLeadershipCommunication

About the role

  • Recent advancements in underlying technology have finally made it possible for AI to impact clinical care in a meaningful way.
  • The candidate will have experience leading Medical Affairs for a cancer focused company.
  • Actual salary may vary based on qualifications and experience.

Responsibilities

  • Lead multidisciplinary teams of data scientists and PhDs to provide external facing communications and customer interactions for the company

Requirements

  • 10 years of clinical practice and experience required

Nice to have

  • MD degree with oncology clinical training (breast oncology preferred)

Compensation

  • The expected salary range above is applicable if the role is performed from New York and may vary for other locations (California, Colorado, Illinois).
  • Actual salary may vary based on qualifications and experience.
  • Tempus offers a full range of benefits, which may include incentive compensation, restricted stock units, medical and other benefits depending on the position.
  • $365,000- $475,000

Benefits

  • Serve as a physician leader and speaker for Tempus' Medical Affairs department
  • Help establish and build clinical relationships and health system partnerships.
  • Provide medical input into commercial strategies, disease-state speaker programs, and marketing materials to ensure scientific integrity.
  • Provide medical oversight to elevate corporate documentation and marketing materials through the medical legal review process
  • We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

Equal opportunity

  • We are an equal opportunity employer.

This listing is sourced directly from Tempus AI's careers page and normalized into a canonical job model.