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How CareerBuilder Uses Innovation to Solve Talent Acquisition Problems

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Scott Helmes introduces us to CareerBuilder's experts on talent acquisition and the innovations they are creating to solve the industry's biggest problems.

Published in: Recruiting & HR
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How CareerBuilder Uses Innovation to Solve Talent Acquisition Problems

  1. 1. 9/7/2017 © 2017 CareerBuilder Learn From the Experts: How CareerBuilder Uses Innovation to Solve Talent Acquisition Problems ML/AI Edition
  2. 2. 9/7/2017 © 2017 CareerBuilder #CBEmpower Learn from the Experts © 2017 CareerBuilder2
  3. 3. 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
  4. 4. 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
  5. 5. 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.
  6. 6. 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.
  7. 7. 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”.
  8. 8. 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.
  9. 9. 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.
  10. 10. 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.
  11. 11. Classification of Job Seekers | Browse Concept © 2017 CareerBuilder11
  12. 12. What Self Driving Cars See © 2017 CareerBuilder12
  13. 13. User Experience Should Drive Good Design © 2017 CareerBuilder13
  14. 14. Using ML/AI To Intelligently Suggest Candidates © 2017 CareerBuilder14 Searches I Ran Yesterday
  15. 15. Using ML/AI To Intelligently Suggest Candidates © 2017 CareerBuilder15 From All My Sources
  16. 16. Semantic Search in RDB © 2017 CareerBuilder16
  17. 17. 9/7/2017 © 2017 CareerBuilder #CBEmpower Research and Development
  18. 18. 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
  19. 19. 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
  20. 20. Context Around Skills | Search for Sales Professionals © 2017 CareerBuilder20 Helpful Less Helpful
  21. 21. Observed Signals Inferred Signals Likelihood To Respond © 2017 CareerBuilder21 Candidate Tenure Is Beyond The Expected Tenure For This RoleCandidate Active Yesterday
  22. 22. Will Robots Take My Job? 22 http://www.bbc.com/news/technology-34066941
  23. 23. We’re Safe. © 2017 CareerBuilder23
  24. 24. 9/7/2017 © 2017 CareerBuilder #CBEmpower Semantic Search
  25. 25. 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
  26. 26. What is Semantic Search? Find what you mean ... not what you type Search for things ... not for strings
  27. 27. 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
  28. 28. 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
  29. 29. The job is automatically converted into a search query.. .. and matched to the candidates in your database(s) ... and vice versa! Semantic Expansion Example
  30. 30. 9/7/2017 © 2017 CareerBuilder Thank you  Scott.helmes@careerbuilder.com  Twitter: @shelmes How did we do? You can tell us now by visiting the app to provide your review.

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