The Fifth Elephant 2019

Gathering of 1000+ practitioners from the data ecosystem

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Accelerating Hiring with Data Science

Submitted by Vishal Gupta (@vizgupta) on Monday, 15 April 2019

Session type: Short talk of 20 mins

Abstract

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.

Outline

  • 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

Speaker bio

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.

Comments

  • Zainab Bawa (@zainabbawa) Reviewer 5 months ago

    We’ll need to see draft slides to understand:

    1. What is the problem that you are trying to solve? How can this be extrapolated beyond Freshworks’ use case? Participants at The Fifth Elephant don’t want to listen to company specific problems. They want to hear case studies, war stories or insightful analyses of new systems introduced to solve existing problems.
    2. Why is AI the solution to the hiring problem? Couldn’t the same problem be solved with some other techniques?
    3. What approaches did you consider before deciding on AI?
    4. What is the cost of this exercise with AI? How do these costs compare if you were to use other solutions?
    5. What can participants, who don’t have hiring problems, learn from this case study? What is the takeaway for the audience?

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