Presented By O'Reilly and Cloudera
Make Data Work
22–23 May 2017: Training
23–25 May 2017: Tutorials & Conference
London, UK

Ask me anything: Data science applications and deployment

Angie Ma (Faculty), Scott Stevenson (Faculty)
14:5515:35 Thursday, 25 May 2017
Ask Me Anything
Location: Capital Suite 7
Average rating: ****.
(4.00, 1 rating)

Angie Ma and Scott Stevenson share their experience and lessons learned from having worked on over 160 commercial data science projects with over 120 organizations from different sectors and industries.

Topics include:

  • A pragmatic approach to data science, data science pipelines, and machine learning for different vertical applications
  • Lessons from implementing and deploying models for production
  • Building data science capabilities—transformation, roadmap, tooling, training existing staff, bringing in new staff to take on data science, and engineering challenges for large corporations
  • Where are the unicorns? How to find and hire great data scientists and how to evaluate their skill sets
  • How to build and manage an innovative data science team that integrates tightly with all business functions and delivers business value
Photo of Angie Ma

Angie Ma


Angie Ma is a cofounder and chief operating officer of Faculty, a London-based AI technology company that provides products and services in strategy, software, and skills. Faculty has delivered more than 300 commercial data science projects across 23 sectors and 8 countries. Angie is passionate about real-world applications of machine learning that generate business value for companies and organizations and has experience delivering complex projects from prototyping to implementation. She supports senior leaders to build AI capability, advising on skills transformation. A physicist by training, previously, Angie was a researcher in nanotechnology working on developing optical detection for medical diagnostics.

Photo of Scott Stevenson

Scott Stevenson


Scott Stevenson is a senior data scientist at Faculty, where he develops and deploys state-of-the-art machine learning models. He leads Faculty’s research into the use of deep learning for realistic speech synthesis, and architected core components of the Faculty platform. His work has been featured by the BBC, CNBC, Sky, and The Telegraph. Outside of Faculty, he maintains and contributes a range of open source software. Scott holds a DPhil in Particle Physics from Oxford University, and before joining Faculty carried out fundamental physics research at CERN and Stanford University.