How CareerBuilder Uses Innovation to Solve Talent Acquisition Problems

9/7/2017 © 2017 CareerBuilder
Learn From the Experts: How CareerBuilder Uses
Innovation to Solve Talent Acquisition Problems
ML/AI Edition

#CBEmpower
Learn from the Experts
© 2017 CareerBuilder2

Introduction
3
As VP of Global Services Strategy, Matt
focuses on the edges of CareerBuilder’s
products, ensuring that they drive recruiter
efficiency by integrating well with each
other and with the tools recruiters use daily.
His 12 years of experience in the
recruitment space has made him passionate
about applying data science, running high
scale microservices, and bringing it all
together into a recruiter friendly sourcing
product.
Matt McNair
CareerBuilder

Introduction
4
Rob Houser, Senior Director of UX, has
worked in the field of user experience for
25 years with companies such as NCR
Corporation (Retail Solutions) and Sage
Software (Accounting/ERP, Payroll, and
HCRM software). Rob manages a team of
user researchers, interaction designers, and
visual designers who work on
CareerBuilder’s software products.
Rob Houser
CareerBuilder

What is Automation?
5
Automation is the use of various
control systems to move manual
operations or those done by
humans to processes that require
minimal or reduced human
intervention.

What is Machine Learning?
6
Machine Learning is a current
application of AI based around the
idea that we should really just be
able to give machines access to
data and let them learn for
themselves.

What is Artificial Intelligence?
7
Artificial Intelligence is the broader
concept of machines being able to
carry out tasks in a way that we
would consider “smart”.

What is a Knowledge Graph?
8
The Knowledge Graph is
a knowledge base used by Google to
enhance its search engine's search
results with semantic-search
information gathered from a wide
variety of sources.

What is a Deep Learning?
9
Deep learning (also known as deep
structured learning or hierarchical learning)
is the application of artificial neural
networks (ANNs) to learning tasks that
contain more than one hidden layer. Deep
learning is part of a broader family
of machine learning methods based
on learning data representations, as
opposed to task-specific algorithms.

What is a Unified Taxonomy?
10
Taxonomic data form a substantial, but
scattered, resource. The alternative to such
a fragmented system is a 'unitary' one of
preferred, consensual classifications. For
effective access and distribution the (Web)
revision for a given taxon would be
established at a single Internet site.

Classification of Job Seekers | Browse Concept

What Self Driving Cars See

User Experience Should Drive Good Design

Using ML/AI To Intelligently Suggest Candidates
Searches I Ran Yesterday

Using ML/AI To Intelligently Suggest Candidates
From All My Sources

Semantic Search in RDB

#CBEmpower
Research and Development

Introduction
18
Yun Zhu is the Lead Data Scientist of Applied
Machine Learning team at CareerBuilder.
His team builds the underlying machine
learning models that CareerBuilder
products rely on.
Name
Company Name
Yun Zhu
CareerBuilder

Introduction
19
Khalifeh Al Jadda is the lead data scientist
on the Search Data Science Team. He has
experience implementing large scale,
distributed machine learning algorithms to
solve challenging problems in domains
ranging from Bioinformatics to search and
recommendation engines. He leads the data
science projects to improve search
relevancy and build a new recommendation
engine leveraging AI, Machine Learning, and
Big Data frameworks.
Name
Company Name
Khalifa AlJadda
CareerBuilder

Context Around Skills | Search for Sales Professionals
Helpful
Less Helpful

Observed Signals Inferred Signals
Likelihood To Respond
Candidate Tenure
Is Beyond The
Expected Tenure
For This RoleCandidate Active
Yesterday

Will Robots Take My Job?
22 http://www.bbc.com/news/technology-34066941

We’re Safe.

#CBEmpower
Semantic Search

Introduction
25
Founder and CEO of Textkernel (2001). Since 2015
part of CareerBuilder group. Leads team of 110
people, most tech experts. R&D background in AI,
Machine Learning & NLP since 1990.
Born in Prague, lives in Amsterdam, married, 2
teenage daughters. Eclectic music tastes.
Semantic Recruitment / Labour Market Analysis /
Natural Language Processing / Machine Learning /
Search & Match / Enterprise Software
@jakubzavrel zavrel@textkernel.nl
Jakub Zavrel
Textkernel

What is Semantic Search?
Find what
you mean
...
not what
you type
Search for
things
...
not for
strings

Semantic Search & Match. Why?
More relevant candidates, faster!
1. More candidates: Expanding words to concepts
No guessing what candidate wrote in the resume
2. Relevant candidates: Better filtering and ranking
Avoid wrong matches by understanding context
3. Work faster
- Start working on the most promising candidates first
- Be a great searcher without advanced Boolean search
- Use documents (jobs, resumes) to start automated searches
A twoword user query
= 35+ system query

3 Elements of Semantic Search
Document
understanding
Domain
knowledge
Machine
Learning
➔CV & job parsing
➔Information
extraction
➔Taxonomies
➔Ontologies
➔Skills
➔Keywords
➔Algorithms learn
from a large set
of data
➔Learning to Rank
➔Deep Learning

The job is
automatically
converted into a
search query..
.. and matched to the
candidates in your
database(s)
... and vice versa!
Semantic Expansion Example

Thank you
 Scott.helmes@careerbuilder.com  Twitter: @shelmes
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How CareerBuilder Uses Innovation to Solve Talent Acquisition Problems

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