'Not Interested' Response — Data Scientists & Analysts

Variables to fill in: {{role_summary}}{{candidate_info}}{{channel}}

You are a sourcing specialist whose outreach gets replies from candidates who ignore every other recruiter. I need your help in the context of data science and analytics candidates with mixed research and industry backgrounds.

TASK: Respond to a soft no in a way that builds the long-term pipeline.

DELIVERABLE: Produce a gracious reply that leaves a strong impression, one calibrated question to learn what would change their mind, and a future-touch note format.

PROCESS:
1. Review the inputs below. If anything critical is missing or ambiguous, ask me up to three clarifying questions before producing the deliverable.
2. Use the inputs to find the one thing that makes this role a genuine upgrade for THIS candidate — comp, scope, tech, mission, or manager.
3. Personalize on their work, not flattery; reference something specific they built, wrote, or achieved.
4. Keep first messages short with a low-commitment ask; save the full pitch for after they engage.
5. Produce the deliverable in a clean, skimmable format I can use directly.

QUALITY BAR:
- Zero recruiter-spam markers: no 'exciting opportunity', no 'perfect fit', no bait-and-switch.
- Reads like it could only have been sent to this one person.
- Honest about the role's constraints — surprises kill candidates later anyway.

INPUTS:
- The role: title, level, comp range, team, and the honest pitch: {{role_summary}}
- What you know about the candidate: profile, work, current situation: {{candidate_info}}
- Where you're reaching them: LinkedIn, email, community, referral: {{channel}}

Run this prompt on a real model without leaving the page. Every run is saved to your history for this prompt.

How to use

1. 1Fill in the {{role_summary}}, {{candidate_info}}, {{channel}} variables with your real details — specifics in, specifics out.
2. 2Paste the prompt into ChatGPT, Claude, Gemini, or any capable model.
3. 3Answer the clarifying questions it asks; that step is what makes the output fit your situation.
4. 4Iterate on the deliverable: ask for alternatives, tighter versions, or a different angle on any section.

Pro tips

• If the output feels generic, add more concrete detail to the inputs — names, numbers, and constraints sharpen everything.
• Works well in a thread: keep the conversation going to refine a gracious reply that leaves a strong impression, one calibrated question to learn what would change their mind, and a future-touch note format.