Eric Nyabiosi

Eric Nyabiosi

Job Title: VP, Data Science, Head of AI ML Predictive Modeling

Company: Comcast

Eric Nyabiosi is an analytics executive, data strategist and innovator. Currently, Eric is the head of Data Science for Comcast Corporation, one of the world’s largest communications | media | entertainment | technology company. In this capacity, he specializes in creating value by providing executive leadership in the areas of artificial intelligence, machine learning, predictive modeling and optimization. He informs decision making across risk management, pricing strategy, marketing analytics and customer experience by synthesizing actionable insights from big data.

Prior to joining Comcast, Eric headed the AI Specialized Analytics team at Citi, headed the financial planning and analytics team at Toyota, built risk mitigation products at JPMorgan Chase and held other diverse analytics roles with Fortune 500 companies. Eric is passionate about all things data – from AI, ML, DL, NLP, modeling, simulation, experimental designs, to data analytics, data visualization and creating user interfaces that help people with varied experiences makes sense of data, learn from data and tell stories from data.

Aside from his passion for data, Eric has a lifelong commitment on giving back to the community and partnering in missions that produce lifesaving outcomes. He has extensive local and international experience serving others as a volunteer, member, sponsor, board member and co-founder for several organizations that focus on providing mentoring, education, sustainable development and disaster relief efforts.

Speaking at the following:

09:15am - 09:45am
Predicting and Preventing Payment Fraud Through Data Science

Businesses lose more than $3.5 trillion to fraud each year, a massive and systematic drain on profit – how can Data Science reduce these loses? Classic machine learning techniques can suffer from low fraud detection rates and a massive amount of false positives – what alternatives are there and how can we optimize the process?… Read more.