Creating Jobs

Jobs are the foundation of your hiring workflow in Stori. Each job has its own interview questions, pipeline, candidates, and benchmarks.

Stori uses AI to generate tailored interview questions for each job. The AI reads your job title and description and generates 3-5 behavioral and situational questions designed to evaluate candidates for this specific role.

How it works:

• After creating your job, Stori automatically generates interview questions based on the job description.
• Questions are a mix of behavioral questions ("Tell me about a time when...") and situational questions ("How would you handle...").
• Each interview contains 3-5 questions for candidates to answer on video.
• You can review, edit, reorder, add, or remove questions before publishing.

Question types:

• Behavioral — "Tell me about a time when you had to manage a difficult stakeholder." These reveal how candidates have handled real situations in the past.
• Situational — "How would you approach building a new team from scratch?" These reveal how candidates think through problems and plan ahead.

Every job has a built-in Kanban pipeline with four stages to help you organize and track candidates through your hiring process:

• Qualified — Candidates who have been sourced, applied, or completed their AI interview. This is the initial stage where all new candidates land.
• Liked — Candidates you have reviewed and are interested in. Move candidates here after reviewing their interview, traits, and profile.
• Invite — Candidates you want to move forward with. Use this stage for candidates you plan to invite to a live interview or next step.
• Final — Your final shortlist of candidates. These are the people you are seriously considering for an offer.

Drag and drop candidate cards between stages on the Kanban board, or use the stage dropdown on a candidate's profile to move them through the pipeline. All team members can see the pipeline and where each candidate stands.

You can manage your jobs from the Jobs page. Here are the actions available:

• Duplicate — Create a copy of an existing job with the same details and interview questions. Useful when hiring for the same role in a different location or team.
• Pause — Temporarily pause a job. Paused jobs stop accepting new candidates but retain all existing candidate data and pipeline progress.
• Close — Mark a job as closed when the position has been filled. Closed jobs are moved out of the active view but all data is preserved.
• Archive — Archive old jobs you no longer need in your active list. Archived jobs can be restored later if needed.

When you create a job and have Sourcing, Stori can automatically generate an AI-powered search strategy to help you find the right candidates in the sourcing database.

How it works:

• Stori reads your job title, description, required skills, and other details.
• The AI generates optimized search filters including job titles to search for, relevant skills, target companies, experience levels, and location criteria.
• The generated strategy is pre-loaded into the sourcing search page, ready for you to review and refine.
• You can adjust any of the AI-suggested filters before running your search.

This saves significant time compared to manually building search queries, especially for complex roles where the right candidates might have various job titles or skill combinations.

The quality of your job description directly impacts the quality of AI-generated interview questions and search strategies. Here are best practices:

• Be specific about responsibilities — List the top 5-7 things this person will do daily, not generic descriptions.
• Separate must-haves from nice-to-haves — Clearly distinguish required skills from preferred ones.
• Include salary range — Jobs with salary ranges attract more qualified candidates and save time filtering.
• Describe the team — Mention who this person will work with and report to.
• Include growth opportunities — Candidates want to know where this role can lead.
• Keep it concise — Aim for 400-800 words. Overly long descriptions reduce completion rates.