Tempus+ Medical Director
Posted Sep 17, 2026 · We last checked this listing on Oct 6, 2026
Likely interview questions for this role
Written from this job description, not a generic list. Each one notes what the interviewer is really checking.
About your past
Tell me about research you led or guided using real-world data. What did it show and how was it used?
What they are checking: Whether they have real evidence-generation experience and understand how findings move into practice.
Describe a time you were the public face of a program or initiative.
What they are checking: Whether they are comfortable representing a program to senior academics and handling tough questions.
How would you work with internal data science, product and partnership teams to scale research output?
What they are checking: Whether they can lead through partnership across teams they do not manage.
What would you do
How would you set a research strategy for a network of academic institutions and health systems using the Tempus+ model?
What they are checking: Whether they can shape a research agenda across many institutions with different priorities and capabilities.
Working Groups bring together thought leaders from competing institutions. How would you keep them productive?
What they are checking: Whether they can run collaborative groups where academic credit and institutional interests may clash.
How would you measure whether the Tempus+ program is succeeding?
What they are checking: Whether they think in terms of publications, adoption and institutional engagement, with concrete measures.
An institution has been in the network for a year and has produced nothing. What do you do?
What they are checking: Whether they diagnose the barrier, such as time, data skills or interest, and act on it.
About the work
How would you help investigators use de-identified multimodal data in Lens to ask good research questions?
What they are checking: Whether they can coach researchers on study design and data limits, not just give them access.
What are the biggest pitfalls in research built on real-world oncology data, and how do you guard against them?
What they are checking: Whether they understand bias, confounding and data quality limits well enough to protect research credibility.
How would you build education into the program so participants keep coming back?
What they are checking: Whether they see education as a driver of adoption and can design useful content for researchers.
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As published by Tempus AI.