Liat Ben
Porat

Customer-driven AI: Making Sure Your Model Suits Your Customer Needs

Intuit

Liat Ben Porat

Liat
Ben Porat

Customer-driven AI: Making Sure Your Model Suits Your Customer Needs

Intuit

Liat Ben Porat

Bio

Liat is a data scientist at Intuit’s Anti-Fraud Data Science team, responsible for delivering high-fidelity risk models to counter fraud threats.

 

Liat’s vast experience and knowledge in the realms of AI-ML and data modeling coupled with anti-fraud experience provide her with unique insights into the design processes inside Intuit. Liat holds a bachelor’s degree in Computer Science and a master’s degree in Applied Mathematics from TAU.

Bio

Liat is a data scientist at Intuit’s Anti-Fraud Data Science team, responsible for delivering high-fidelity risk models to counter fraud threats. Liat’s vast experience and knowledge in the realms of AI-ML and data modeling coupled with anti-fraud experience provide her with unique insights into the design processes inside Intuit. Liat holds a bachelor’s degree in Computer Science and a master’s degree in Applied Mathematics from TAU.

Abstract

Did you ever happen to deliver a well-performing model that no one ended-up using? Did you happen to work a long period on delivering end-to-end solutions just to find out your model output is not useful for its consumers? We can avoid wasting our time and effort if we understand what our customer actually needs. Design Thinking is a method for creative problem-solving that has been broadly adopted by world-class design firms like IDEO and many of the world’s leading brands like Apple, Google, Samsung, and GE.

 

One of the focus of design thinking is rapidly experimenting and prototyping your product in multiple phases of development to ensure full alignment between customer needs and model’s stakeholders. In this round table, we will briefly introduce a few of the principles & methods for customer-driven innovation as they applied in Intuit – including conducting ‘follow me home’s, setting up customer benefit metrics, performing multiple experiments beyond AB testing and designing rapid experiments for fast, valuable feedback. We will brainstorm & share the best practices to ensure our work as data scientists is aligned with the customer’s needs, discuss the potential trade-offs, and deep dive into the various ways in which we can ensure optimal customer’s delight.

Abstract

Did you ever happen to deliver a well-performing model that no one ended-up using? Did you happen to work a long period on delivering end-to-end solutions just to find out your model output is not useful for its consumers? We can avoid wasting our time and effort if we understand what our customer actually needs. Design Thinking is a method for creative problem-solving that has been broadly adopted by world-class design firms like IDEO and many of the world’s leading brands like Apple, Google, Samsung, and GE.

 

One of the focus of design thinking is rapidly experimenting and prototyping your product in multiple phases of development to ensure full alignment between customer needs and model’s stakeholders. In this round table, we will briefly introduce a few of the principles & methods for customer-driven innovation as they applied in Intuit – including conducting ‘follow me home’s, setting up customer benefit metrics, performing multiple experiments beyond AB testing and designing rapid experiments for fast, valuable feedback. We will brainstorm & share the best practices to ensure our work as data scientists is aligned with the customer’s needs, discuss the potential trade-offs, and deep dive into the various ways in which we can ensure optimal customer’s delight.

Discussion Points

  • Data scientist role definitions – full stack data scientists vs. specialisations
  • Pure data science teams vs embedded teams
  • Data science reporting lines
  • Professional and personal development in embedded teams

Discussion Points

  • Data scientist role definitions – full stack data scientists vs. specialisations
  • Pure data science teams vs embedded teams
  • Data science reporting lines
  • Professional and personal development in embedded teams

Planned Agenda

Planned Agenda