Below is the two-day PAW Workforce conference program -
see also the pre- and post-event training workshops days.
| Pre-Conference Workshops: Sunday, May 14, 2017 | |
|
Uplift Models: Optimizing the Impact of Your Marketing  Kim Larsen, Stitch Fix |
Big Data: Proven Methods You Need to Extract Big Value  Marc Smith, Connected Action Consulting Group |
| Pre-Conference Workshops: Monday, May 15, 2017 | |||
|
• Room: Nob Hill BC Hadoop for Predictive Analytics: Hands-On Lab James Casaletto, MapR Technologies |
• Room: Salon 5 & 6 R Bootcamp: For Newcomers to R Max Kuhn, RStudio |
• Room: Salon 3 & 4 Supercharging Prediction with Ensemble Models Dean Abbott, Abbott Analytics |
|
• Room: Salon 5 & 6 R for Predictive Modeling: A Hands-On Introduction Max Kuhn, RStudio |
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| Day 1: Tuesday, May 16, 2017 (PAW Business runs in parallel on this day - dual registration required) |
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| 8:00-8:30am | Registration Room: Grand Assembly |
Networking Breakfast Room: Salon 9 |
| 8:30-8:35am | Conference Chair Welcome • Room: Nob Hill BC Greta Roberts, Talent Analytics, Corp. | |
| 8:35-9:20am |
Keynote• Room: Nob Hill BC Making Predictive Analytics Work in Your Enterprise Greta Roberts, Talent Analytics, Corp. |
|
| 9:20-10:10am | Plenary Session • Room: Nob Hill BC What to Optimize? The Heart of Every Analytics Problem John Elder Ph.D., Elder Research |
|
| 10:10-10:40am | Exhibits & Morning Coffee Break • Room: Salon 9 | |
| Track 1 • Business Room: Nob Hill BC |
Track 2 •Technical Room: Nob Hill D |
|
| 10:40-11:25am | ||
| Yin-Yang: Statistical Modeling to Predict Attrition and Productivity/Output Metrics Subhadra Dutta, Twitter Menghan Chen, Twitter |
Use Case: Using a data driven approach to identify Information Security risk among Netflix employees Shankar Vedaraman, Netflix | |
| 11:30am-12:15pm | ||
| Use Case: How Kforce Is Evolving From Pre-Hire Assessments to Predictive Hiring Justin Stevens, Kforce |
The Hero with a Thousand Faces: Data Science and Storytelling Nery Castillo-McIntyre, CSAA Insurance Group |
|
| 12:15-1:45pm | Lunch in the Exhibit Hall • Room: Salon 9 | |
| 1:45-2:35pm |
Keynote • Room: Nob Hill BC How to Use Survival Analytics to Predict Employee Turnover Pasha Roberts, Talent Analytics, Corp. |
|
| Track 1 • Business Room: Nob Hill BC |
Track 2 •Technical Room: Nob Hill D |
|
| 2:40-3:25pm | ||
| Use Case: When Predictive Analytics Isn't Enough How We Got 10X Greater ROI Than We Could with Predictive Models Burke Powers, PolarityTE |
Open Sourced Workforce Analytics: A Live Demonstration of Tools and Techniques for Predictive Modeling Jason Noriega, Chevron |
|
| 3:25-3:55pm | Exhibits & Afternoon Break • Room: Salon 9 | |
| 3:55-4:40pm | ||
| Use Case: How CenturyLink Measures How Well Leaders Manage Their Organizations Emily Pelosi, CenturyLink | Case Study: Provoking Innovation On the Edge of Business with Analytics Aaron Burciaga, Accenture | |
| 4:45-5:30pm | ||
| Predicting and Improving Employee Performance Serena Huang, GE Transportation |
Getting the Most from Your Predictive Models: Exploring the Impact of Segmentation on Common Modeling Techniques Chris Short, Cox Enterprises Inc. | |
| 5:30-7:00pm | Networking Reception • Room: Salon 9 | |
| 7:15pm | Dinner with Strangers | |
| Day 2: Wednesday, May 17, 2017 (PAW Business runs in parallel on this day - dual registration required) |
||
| 8:00-8:30am | Registration Room: Grand Assembly |
Networking Breakfast Room: Salon 9 |
| 8:30-8:35am | Conference Chair Welcome • Room: Nob Hill BC Greta Roberts, Talent Analytics, Corp. |
8:35-9:20am |
Keynote • Room: Nob Hill BC Weird Science: How to Know Your Predictive Discovery Is Not BS Eric Siegel, Predictive Analytics World |
| 9:20-10:10am | Diamond Sponsor Presentation • Room: Nob Hill BC Legal and Ethical Issues when using Predictive Analytics for Talent Acquisition Eric J. Felsberg, Jackson Lewis P.C. |
|
| Track 1 • Business Room: Nob Hill BC |
Track 2 •Technical Room: Nob Hill D |
|
| 10:15-10:35am | ||
| Use Case: Enhancing the Quality of Predictive Modeling on College Enrollment Feyzi Bagirov, Becker College |
Case Study: SmarterHQ When Model Interpretation Matters: Understanding Complex Predictive Models Dean Abbott, SmarterHQ |
|
| 10:40-11:00am | ||
| Using Analytics in Recruitment to Identify Improvement Opportunities Sue Lam, Shell |
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| 11:00-11:30am | Exhibits & Morning Coffee Break • Room: Salon 9 | |
| 11:30am-12:15pm | ||
| The Pay Equity Revolution: How Advanced Analytics are Helping to Close the Gender Pay Gap in Organizations Haig Nalbantian, Mercer |
Getting the most out of NLP for cover letter ranking, resume screening, interview conversations and more
Ben Taylor, HireVue |
|
| 12:15-1:25pm | Lunch in the Exhibit Hall • Room: Salon 9 | |
| 1:30-2:15pm | Keynote • Room: Nob Hill BC Use Case: How Predictive Models Improve Retail Sales Experience and Performance Elpida Ormanidou, Chico's FAS |
|
| Track 1 • Business Room: Nob Hill BC |
Track 2 •Technical Room: Nob Hill D |
|
| 2:15-3:00pm | ||
| Use Case: Changing Talent Management through Predictive Analytics Kevin Zhan, The Advisory Board |
How HR can take a giant leap via Artificial Intelligence Hai Harari, Intel |
|
| 3:00-3:30pm | Exhibits & Afternoon Break • Room: Salon 9 | |
| Track 1 • Business Room: Nob Hill BC |
Track 2 •Technical Room: Nob Hill D |
|
| 3:30-4:15pm | ||
| Use Case: Real World Lessons in Predicting Employee Retention and Engagement Mike Rosenbaum, Arena |
Use Case: Predicting Safety and Performance from Unstructured Data Ben Taylor, HireVue |
|
| 4:15-5:00pm | ||
| Expert Panel • Room: Nob Hill BC Politics, Power Tools and Perfect Data Moderator: Pasha Roberts, Talent Analytics, Corp. Panelists: Elpida Ormanidou, Chicos Nery Castillo-McIntyre, CSAA Insurance Group Ben Taylor, HireVue |
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| Post-Conference Workshops: Thursday, May 18, 2017 | |
|
The Best and the Worst of Predictive Analytics: Predictive Modeling Methods and Common Data Mining Mistakes  Dr. John Elder, Elder Research, Inc. |
Advanced Methods: Data Preparation and Modeling Techniques Dean Abbott, Abbott Analytics |
