Director of User Experience 2018 – 2019

Feedzai provides a Fraud Prevention platform used by large banks (Lloyds, Citibank) and merchants (Nike, Adidas) using machine learning.

While at Feedzai, I expanded the team from 2 to 4 UX Designers, 2 UX Researchers, and 1 Product Marketing Designer. In addition to hiring and training the team, I focused on socializing and implementing a new discovery-driven process that ensured cross-functional team alignment and empowered the UX team to focus efforts on the projects with the highest impact on the success of the product and company.

Building the team

I led a number of activities to attract top-level talent to the team at Feedzai. I created a hiring campaign shared across LinkedIn and Facebook, hosted (and presented at) a couple of local UX Meetups, and worked with my team to create a humorous video illustrating the benefits of doing UX at Feedzai.

Enabling UX impact

While Feedzai had a solid Agile Engineering group when I started, the product organization was not very mature and designs were being done “just in time” for grooming sessions with Engineering. I worked closely with my team and peers to develop a discovery-driven framework to allow UX research and design activities to run ahead of development sprints.

More importantly, each project began with a kick-off where each of the leads (UX, PM, Eng) agree to the goals of the project, the success metrics, and the activities and scope of the discovery, definition, and design phases.

Understanding our users

At Feedzai, there was a huge appetite to develop a deep understanding of our users, their motivations, and workflows. In addition to working on roadmap features, I drove the initiative to have the Research and Design team develop and validate User Personas and User Journeys to ensure the design and development effort was focused on solving the right problems. This comprehensive research was conducted with two very different groups of users.

Data Scientists

These users build and review the performance of machine learning models to evaluate and score transactions for risk.

Fraud Analysts

These users manually review and investigate risky transactions to identify and mitigate fraud.