Skills-based candidate screening: A practical workflow

The hiring challenge

A technology company receives hundreds of applications for a backend engineering role. The hiring manager needs a shortlist by Thursday, which is two days away.

Manual screening requires 25-40 hours. At this volume, qualified candidates are often overlooked because they describe their skills differently from the search terms. Deadline pressure forces decisions based on incomplete information.

The workflow

Step 1: Centralize

Import up to 1000 candidates. TalentsForce auto-converts formats and creates profiles simultaneously.

Upload all resumes in a single action. The platform accepts PDF and Word formats without requiring reformatting or manual entry.

Time investment: 5 minutes

Result: Candidate profiles ready for processing

Step 2: Extract

Extract candidates' information and enrich your organization's skill inventory.

The platform analyzes each resume to identify capabilities. When a candidate writes "Architected microservices handling 10 million requests daily", the system recognizes distributed systems design, API development, scalability expertise, and cloud infrastructure even without those exact terms.

The system normalizes terminology automatically. "JavaScript," "JS," and "ECMAScript" map to the same skill. "Django experience" indicates Python proficiency.

Time investment: 3 minutes

Result: Every profile contains structured, comparable skills data

Step 3: Match

Each candidate profile is now structured in the system, ready for matching with high accuracy.

The platform evaluates all candidates against your role requirements using semantic matching. A candidate who built "FastAPI applications" gets credited for Python and API expertise. Someone with "Terraform on AWS" matches cloud infrastructure requirements.

Each candidate receives an alignment score based on coverage of required skills, presence of preferred skills, and proficiency levels. The system identifies skill gaps for every candidate.

Time investment: 2 minutes

Result: All candidates ranked by capability alignment with role needs

Step 4: Review

Start with a ranked shortlist. Each rank is linked back to the candidate profile in the system.

Instead of reading resumes sequentially, you begin with candidates whose skills align most closely with requirements.

Natali Craig – 87% Match

Required Skills Met:

• Python: 5 years of demonstrated experience
• AWS: Certified, 3 years of production deployment
• API Design: RESTful and GraphQL APIs for customer applications

Preferred Skills Met:

• Docker: Implemented in CI/CD workflow
• Database Optimization: Reduced query time by 60%

Gap Identified:

• Kubernetes: No demonstrated experience

Time investment: 20 minutes to review 10-15 top candidates

Result: Defensible shortlist of 8-10 candidates with documented skills alignment

Desired outcomes

All candidates were assessed using identical criteria. Every shortlisted candidate meets 80% or more of the requirements with verifiable evidence. Personal screening biases don't influence ranking.

Building talent intelligence

All candidate profiles remain as structured skills data. When a DevOps role opens months later, the platform instantly identifies relevant candidates from previous searches, including those not selected before but whose skills match the new requirements.

Each recruiting cycle adds to your skills and intelligence. Former applicants become searchable talent. Your talent pool grows with every posting.

Explore: talentsforce.io