Name
Abstract | ||
|---|---|---|
Due to the rapidly changing, dynamic nature of today's economic landscape, organizations are often engaged in a continuous exercise of matching their workforce with the changing needs of the marketplace. Re-skilling offers these enterprises the ability to effectively manage and retain talent, while also satisfying business requirements. We describe an analytics-based propensity scoring model for re-skilling by combining historical employee job-role/skill records, relationships between different job-roles/skills, employee resumes, and job postings. This is used to determine the source features that are the closest to a required target skill and hence identify employees that can be easily trained for the target skill. We evaluate this approach for a representative set of target skills at a multinational with a large services/consulting arm. We show that the propensity model learnt from the combined data sources has a high accuracy that is also substantially better than that achieved by using features from job-roles or resumes alone. The performance is improved further by using an ensemble model to evaluate the propensity scores. |
Year | Venue | Keywords |
|---|---|---|
2017 | IEEE Global Conference on Signal and Information Processing | HCM,re-skilling,skill adjacency,workforce analytics,resume parsing |
Field | DocType | ISSN |
Multinational corporation,Ensemble forecasting,Workforce,Propensity score matching,Computer science,Knowledge management,Business requirements,Workforce planning,Analytics | Conference | 2376-4066 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Moninder Singh | 1 | 381 | 105.12 |
| Karthikeyan Natesan Ramamurthy | 2 | 163 | 31.33 |
| Vasudevan, S. | 3 | 13 | 2.11 |