Disease-Area Clinician — Trial Endpoints & Prescribing

What you would do

• Create and refine assessment rubrics for evaluating drug efficacy and safety profiles
• Assess clinical trial outcomes and evaluate whether findings would impact prescribing decisions
• Evaluate likelihood of real-world adoption for new drugs in your therapeutic specialty
• Provide detailed written reasoning on clinical significance versus statistical significance of trial data
• Design and score clinical scenarios testing AI understanding of evidence-based prescribing

Who they want

• Board-certified, actively practicing physician (MD or DO) in your specialty
• Deep expertise in a specific disease area or therapeutic category
• Familiarity with biotech and pharma development programs and FDA pathways
• Experience with clinical trials, endpoints, and evidence interpretation in your field
• US-based with availability for 10-20 hours of work over 1-2 week engagement

Main skills

What the interview asks about

1. 1.Parsing complex trial endpoints

   Trials report multiple endpoints with varying effect sizes and statistical power; clinicians must distinguish noise from signal and judge practical importance.

   For example: “A diabetes trial shows the primary endpoint (HbA1c reduction) missed statistical significance, but a secondary endpoint (cardiovascular events) showed benefit. How would you evaluate that drug for your patients?”

2. 2.Prescribing decision trade-offs

   Drugs rarely win on all dimensions; clinicians balance efficacy, safety, cost, and patient factors daily. AI must learn that nuance.

   For example: “Drug A reduces symptom severity 30% with rare but serious side effects; Drug B reduces it 15% with excellent safety. For which patient populations would you choose each, and why?”

3. 3.Therapeutic area context

   Every specialty has unmet needs, competitive alternatives, and practice patterns; your domain expertise reveals where a new drug creates real advantage.

   For example: “You see a new therapy for your specialty area. What would you want to know about how it compares to current options, and what would convince you it's worth switching patients?”

4. 4.Rubric construction for AI evaluation

   Translating clinical judgment into explicit criteria is how AI learns what clinicians actually value - you must be specific enough for AI to score consistently.

   For example: “Design three criteria you'd use to score whether a new drug is clinically meaningful in your specialty. Make each measurable and grounded in actual prescribing decisions.”

5. 5.Uncertainty and incomplete evidence

   Rare conditions, emerging data, and trial limitations are reality; clinicians make judgments despite imperfect information. AI must learn that pragmatism.

   For example: “A Phase II trial in a rare disease shows promising efficacy but small sample size. How would you advise a patient considering this drug, and what additional data would change your stance?”

A task you may get

Review abstracts from two drugs in your specialty (one approved, one in development). For each, create a 1-page rubric scoring clinical significance, real-world applicability, and competitive advantage. Justify each criterion choice.

How to prepare

• Compile 2-3 recent clinical trials from your therapeutic area and review outcomes and implications
• Document your clinical practice patterns: how you currently select between treatment options and what drives those decisions
• Identify key unmet needs and gaps in current therapies in your specialty - these shape how you evaluate new options
• Review FDA guidance or regulatory pathways relevant to your disease area to understand development timelines and approval criteria

The facts

Pay

$150–230/hr

Hours

Part time, 15 hours a week

Where

Remote · Remote — US based or deep US market experience

Field

Medicine

Posted

8/5/2026

Places left

5

We wrote this page from the public Mercor listing. It may be out of date, so read the full posting before you apply.