Programme Parameters
Advanced Postgrad
8 Weeks (80 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI in Recruitment and Talent Acquisition shows how machine learning is reshaping how organisations find and choose people. You explore the applications transforming the hiring funnel: automated resume parsing and candidate matching, sourcing and ranking, chatbots for candidate engagement, and predictive models for selection and fit. Because these systems make decisions about people’s careers and face rising legal scrutiny, fairness, bias and transparency are central threads, not afterthoughts. You finish able to apply AI to recruitment in a way that is both effective and defensible. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course applies AI to recruitment — candidate sourcing and screening, resume parsing and matching, and predictive selection, with a strong emphasis on fairness and bias.
📋 Course Objectives
1. Automate resume parsing and candidate matching.
2. Apply AI to sourcing and candidate ranking.
3. Build predictive selection and fit models.
4. Audit hiring models for bias and fairness.
5. Ensure transparency and compliance in AI hiring.
👥 Who Should Enroll?
• Recruiters and talent-acquisition professionals
• HR technology and people-analytics teams
• Data scientists working in HR
• Students of people analytics
🚀 Key Learning Outcomes
• The ability to apply AI across the hiring funnel.
• A recruitment-analytics project.
• A fairness-first approach to hiring AI.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
The Hiring Funnel and Its Failure Points
• Sourcing, screening, assessment and offer as distinct decision points
• Where automation compounds existing bias rather than removing it
• Defining the job requirement before automating the search for it
Search, Matching and Outreach
• Semantic matching of candidates to requirements and its blind spots
• Skills-based rather than credential-based matching
• Outreach automation and candidate experience
Screening and Selection Tools
• Resume parsing and the proxies that encode discrimination
• Structured interviews, work samples and their superior predictive validity
• Video and game-based assessment, and the weak evidence behind some vendors
Legal Exposure and Bias Testing
• Adverse impact analysis and the four-fifths rule
• Bias audit obligations including NYC Local Law 144 and EU AI Act high-risk duties
• Candidate notice, explanation and human review rights
Deploying Responsibly
• Vendor diligence: validity evidence, not marketing claims
• Monitoring outcomes by group after deployment
• Measuring quality of hire rather than time-to-fill alone
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | Jupyter Notebook |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Microsoft Excel |
| Covered Tool / Platform | Relevant Online Databases |
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