Accelerating Hiring with Data Science
At Freshworks, we receive more than 1000 applications every week. This leads to a lot of applications for our Talent Acquisition teams to process, which can be difficult. Conventionally, candidate screening at Freshworks has involved a manual review of the candidate’s resume/portfolio which cannot be scaled for smaller HR teams. We experimented with making this process smoother by implementing an automated pipeline that uses data science to score a candidate based on their skills, experience and how suitable they are for the job they have applied for, using thousands of previously processed candidates.
- The recruitment pipeline at Freshworks
- Common roadblocks in hiring and how Data Science can eliminate them
- Building Candidate Scoring models
- Data sources and Enrichment
- Picking the right features for scoring candidates
- Evaluating the Candidate scoring models
- Identifying features that differentiate good candidates
- How candidate scoring models accelerate hiring
- Candidate conversion rates
- Average turnaround time
- AI solutions for the HR in areas other than hiring
- Targetted training modules
- Predicting attrition
As a part of the Data Science team, I work on analysing textual content in deal pipelines and building AI models to boost sales. I enjoy applying AI to solve practical problems.