Good Morning Everyone,
Happy Morning.
Could you please provide inputs on the questions below? Your insights will be helpful for everyone in the current job market situation in India.
1. What are the biggest pain points you face while hiring through job portals?
2. How effective are the current filtering and matching algorithms in finding the right candidates?
3. Do you think an AI-powered ATS can improve your hiring process? If so, what features would be most helpful?
4. How do you verify candidate authenticity before shortlisting? Would a built-in background check system help?
5. What additional insights (analytics, hiring trends, candidate behavior) would help improve recruitment decisions?
Regards,
Bhavya Digi Solutions
From India, Hyderabad
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Happy Morning.
Could you please provide inputs on the questions below? Your insights will be helpful for everyone in the current job market situation in India.
1. What are the biggest pain points you face while hiring through job portals?
2. How effective are the current filtering and matching algorithms in finding the right candidates?
3. Do you think an AI-powered ATS can improve your hiring process? If so, what features would be most helpful?
4. How do you verify candidate authenticity before shortlisting? Would a built-in background check system help?
5. What additional insights (analytics, hiring trends, candidate behavior) would help improve recruitment decisions?
Regards,
Bhavya Digi Solutions
From India, Hyderabad
Acknowledge(0)Amend(0)
Insights on Hiring Challenges in the Current Job Market in India
1. Biggest Pain Points While Hiring Through Job Portals
Quality of Applications – A high percentage of irrelevant applications due to poor keyword-based matching.
Duplicate & Fake Profiles – Many job seekers create multiple profiles, leading to redundancy.
Candidate Engagement – Many applicants do not respond after applying or drop off midway.
Lack of Skill Validation – Difficulty in verifying actual skills from resumes without tests or certifications.
Manual Screening Effort – Sorting through hundreds of applications is time-consuming.
Fake Experience & Fraudulent Claims – Exaggerated or falsified work experience is a frequent issue.
2. Effectiveness of Current Filtering & Matching Algorithms
Keyword-based Matching – Often ineffective as it does not assess candidate fit based on experience, intent, or actual skills.
Overlooked Talent – Good candidates may be ignored if their profiles are not keyword-optimized.
Bias in Recommendations – Filtering heavily relies on rigid parameters, missing out on potential candidates who could be trained.
Limited Behavioral Insights – Algorithms do not analyze a candidate’s response rate, application patterns, or job stability history.
3. AI-powered ATS for Improved Hiring & Key Features
An AI-driven Applicant Tracking System (ATS) can streamline recruitment by:
✅ Smart Resume Screening – AI-driven parsing based on skills, experience, and role fit rather than just keywords.
✅ Predictive Candidate Matching – Using machine learning to predict candidate success based on hiring history.
✅ Automated Initial Screening – AI chatbots or video interview pre-screening.
✅ Skill Assessment Integration – AI-based coding tests, psychometric tests, and industry-specific assessments.
✅ Candidate Engagement & Follow-ups – Automated reminders, chatbots for scheduling interviews, and nudges for unresponsive candidates.
✅ Bias Reduction – AI-driven evaluation to avoid gender, ethnicity, and age biases in hiring.
4. Verifying Candidate Authenticity Before Shortlisting
Cross-verification with Previous Employers – Currently, recruiters manually check references.
Background Verification (BGV) Checks – Usually done post-offer, which delays hiring.
Fake Certifications & Degrees – Candidates often upload unverifiable credentials.
Built-in Background Check System? – YES, a real-time BGV system integrated with government & professional databases would be highly beneficial. It could include:
🔹 Aadhar & PAN-based identity verification.
🔹 AI-driven fraud detection for manipulated resumes.
🔹 Automated reference & employment history verification.
5. Additional Insights for Better Hiring Decisions
📊 Hiring Analytics:
Industry-wide salary benchmarking.
Competitor hiring trends.
Demand-supply gap for key skills in specific locations.
📈 Candidate Behavior Insights:
Candidate’s job change frequency & stability.
Response rate & interview attendance probability.
Skill development trends (courses, certifications).
💡 Predictive Hiring Insights:
AI-based hiring success probability per candidate.
“Flight risk” analysis – likelihood of a candidate leaving within a short time.
Talent mapping based on future industry demands.
From India, Gurugram
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281. Biggest Pain Points While Hiring Through Job Portals
Quality of Applications – A high percentage of irrelevant applications due to poor keyword-based matching.
Duplicate & Fake Profiles – Many job seekers create multiple profiles, leading to redundancy.
Candidate Engagement – Many applicants do not respond after applying or drop off midway.
Lack of Skill Validation – Difficulty in verifying actual skills from resumes without tests or certifications.
Manual Screening Effort – Sorting through hundreds of applications is time-consuming.
Fake Experience & Fraudulent Claims – Exaggerated or falsified work experience is a frequent issue.
2. Effectiveness of Current Filtering & Matching Algorithms
Keyword-based Matching – Often ineffective as it does not assess candidate fit based on experience, intent, or actual skills.
Overlooked Talent – Good candidates may be ignored if their profiles are not keyword-optimized.
Bias in Recommendations – Filtering heavily relies on rigid parameters, missing out on potential candidates who could be trained.
Limited Behavioral Insights – Algorithms do not analyze a candidate’s response rate, application patterns, or job stability history.
3. AI-powered ATS for Improved Hiring & Key Features
An AI-driven Applicant Tracking System (ATS) can streamline recruitment by:
✅ Smart Resume Screening – AI-driven parsing based on skills, experience, and role fit rather than just keywords.
✅ Predictive Candidate Matching – Using machine learning to predict candidate success based on hiring history.
✅ Automated Initial Screening – AI chatbots or video interview pre-screening.
✅ Skill Assessment Integration – AI-based coding tests, psychometric tests, and industry-specific assessments.
✅ Candidate Engagement & Follow-ups – Automated reminders, chatbots for scheduling interviews, and nudges for unresponsive candidates.
✅ Bias Reduction – AI-driven evaluation to avoid gender, ethnicity, and age biases in hiring.
4. Verifying Candidate Authenticity Before Shortlisting
Cross-verification with Previous Employers – Currently, recruiters manually check references.
Background Verification (BGV) Checks – Usually done post-offer, which delays hiring.
Fake Certifications & Degrees – Candidates often upload unverifiable credentials.
Built-in Background Check System? – YES, a real-time BGV system integrated with government & professional databases would be highly beneficial. It could include:
🔹 Aadhar & PAN-based identity verification.
🔹 AI-driven fraud detection for manipulated resumes.
🔹 Automated reference & employment history verification.
5. Additional Insights for Better Hiring Decisions
📊 Hiring Analytics:
Industry-wide salary benchmarking.
Competitor hiring trends.
Demand-supply gap for key skills in specific locations.
📈 Candidate Behavior Insights:
Candidate’s job change frequency & stability.
Response rate & interview attendance probability.
Skill development trends (courses, certifications).
💡 Predictive Hiring Insights:
AI-based hiring success probability per candidate.
“Flight risk” analysis – likelihood of a candidate leaving within a short time.
Talent mapping based on future industry demands.
From India, Gurugram
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