From startups to traditional financial institutions, what are the biggest challenges in fintech and where is data science most impactful? Join VP of Product and Data Science at FIS Christine Hurtubise – formerly at fintech startups Orum and Stash – in conversation with Jaclyn Rice Nelson, co-founder of Tribe AI.
Jaclyn Rice Nelson spent the majority of her career at Google partnering with enterprise companies and incubating new products. She was a founding member of the growth team at CapitalG, Alphabet’s growth equity firm, where she advised growth-stage tech companies like Airbnb and Stripe on scaling their technical infrastructure, data security, and leveraging machine learning for growth. In 2019, Jackie and Noah Gale founded Tribe AI to make AI more accessible to companies of all sizes and industries, while also building a new type of career path for top technical talent that emphasizes specialization and freedom. (Read more about it here.) Now, Tribe AI is a highly selective community of 150 machine learning engineers, researchers, and data scientists from industry leaders like Google, Tesla, and Netflix, helping companies solve their toughest business problems using ML.
Christine Hurtubise is currently VP of Product Management and Data Science at FIS, a multinational corporation offering financial technology solutions in three segments: Merchant, Banking, and Capital Markets. She previously led and started the Data Science departments at Stash Invest, a series G personal finance app and Orum.io, a series B payments infrastructure platform. Christine also held leadership roles in the Risk department at OnDeck Capital, where the team used machine learning for automated SMB lending, portfolio management, and allowance setting. She holds a degree in Mathematics from the University of Pennsylvania, magna cum laude.
Want more insights? Join Tribe AI and Scale AI for Applied AI: a series of conversations around how ML is accelerating change across industries. You’ll hear from technical experts at top companies on how they’re using data to drive impact, operationalizing ML solutions, and accelerating adoption across fintech, healthcare, investing, media, and more.