Senior Manager, Data Scientist - Travel Intelligence

Capital One•McLean, VA

4d•Onsite

About The Position

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 You will be a member of the Travel Intelligence team, a high-impact group dedicated to building the "intelligent core" of Capital One Travel. Our mission is to transform vast amounts of travel data into personalized, seamless experiences for our customers. Specifically, this role focuses on the Hotels domain, where you will own the end-to-end technical strategy for Search & Sort and Pricing Optimization. We operate in a "green-field" environment, moving from offline, historical data models to sophisticated, real-time Machine Learning solutions. As part of Travel Intelligence, you won't just be building models; you'll be partnering closely with Product and Engineering to define key metrics (like MOB and Reference Price coverage) and architecting the systems that determine what our customers see and what they pay. This is a unique opportunity to apply the 80:20 rule—starting with pragmatic, high-impact wins while simultaneously building the business case and technical roadmap for the future of ML at Capital One Travel. 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, Conda, AWS, H2O, Spark, and more — to reveal the insights hidden within huge volumes of numeric and textual data Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation Flex your interpersonal skills to translate the complexity of your work into tangible business goals

Requirements

Currently has, or is in the process of obtaining one of the following with an expectation that the required degree will be obtained on or before the scheduled start date: A Bachelor's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 7 years of experience performing data analytics
A Master's Degree in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) or an MBA with a quantitative concentration plus 5 years of experience performing data analytics
A PhD in a quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Computer Science, or a related quantitative field) plus 2 years of experience performing data analytics
At least 2 years of experience leveraging open source programming languages for large scale data analysis
At least 2 years of experience working with machine learning
At least 2 years of experience utilizing relational databases

Nice To Haves

PhD in “STEM” field (Science, Technology, Engineering, or Mathematics) plus 4 years of experience in data analytics
At least 1 year of experience working with AWS
At least 5 years’ experience in Python, Scala, or R for large scale data analysis
At least 5 years’ experience with machine learning

Responsibilities

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, Conda, AWS, H2O, Spark, and more — to reveal the insights hidden within huge volumes of numeric and textual data
Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation
Flex your interpersonal skills to translate the complexity of your work into tangible business goals