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Senior Manager, Data Scientist - US Card (Generative AI Systems)
Capital One
McLean, USA
Full-time
Senior · 7+ years experience
Discovered 1 weeks ago
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Data is at the center of everything we do.
As a startup, we disrupted the credit card industry by individually personalizing every credit card offer using statistical modeling and the relational database, cutting edge technology in 1988!
Fast-forward a few years, and this little innovation and our passion for data has skyrocketed us to a Fortune 200 company and a leader in the world of data-driven decision-making.
As a Data Scientist at Capital One, you’ll be part of a team that’s leading the next wave of disruption at a whole new scale, using the latest in computing and machine learning technologies and operating across billions of customer records to unlock the big opportunities that help everyday people save money, time and agony in their financial lives.
Team Description
The Generative AI Systems (Genesis) team within Card Data Science builds state-of-art, generative AI-based solutions for dialogue, text summarization, reading comprehension, speech recognition, image/document processing as well as time-series sequencing modeling.
We partner with product, tech and design teams to deliver internal applications based on these solutions that drive efficiency in our business and data analytics teams, as well as customer-facing applications that enhance the customer experience.
You will work with a seasoned group of natural language processing (NLP), speech, and computer vision specialists, experimenting with emerging technologies in generative AI, delivering software implementing these technologies, and contributing research to major NLP and AI/ML conferences.
Role Description
In this role, you will:
Partner with a cross-functional team of data scientists, software engineers, and product managers to deliver a product customers love
Leverage a broad stack of technologies — Python, Kubernetes, AWS, H2O, Spark, and more — to reveal the insights hidden within huge volumes of numeric and textual data
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