Capital One - Cambridge, MA
posted 3 months ago
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. Consumer Identity ML is the data science and machine learning team inside Capital One's AI foundation organization. We deliver real-time, personalized, intelligent customer experiences in Capital One's suite of award-winning digital products, including our website, mobile app, emails, chatbot, and beyond. We partner closely with our product and engineering teams to build the data and modeling platforms crucial to the deep understanding of customers that enables our applications to delight them by adapting to their needs. In Consumer Identity ML, you will work at all phases of the data science life cycle, including building machine learning models through all phases of development, from design through training, evaluation and validation, and partnering with engineering teams to improve operationalization in scalable and resilient production systems that serve 50+ million customers. You will also partner closely with a variety of business and product teams across Capital One to conduct the experiments that guide improvements to customer experiences and business outcomes in domains like marketing, servicing and fraud prevention. Additionally, you will write software (Python, e.g.) to collect, explore, visualize and analyze numerical and textual data (billions of customer transactions, clicks, payments, etc.) using tools like Spark.