Senior Associate, Data Scientist - Corporate Strategy

Capital One•McLean, VA

1d•Hybrid

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 The Corporate Strategy team advises our company’s CEO and senior leadership on the most critical decisions facing the company across financial services, technology, and beyond. We engage in a discrete number of high impact issues that drive the performance and direction of the company. Projects focus primarily on formulating enduring and profitable growth and value enhancement strategies and then working with our business and staff teams to ensure the maximum impact. The Integrated Data Engineering, Analysis, and Science—IDEAS—team within Corporate Strategy, creates insights on consumer behavior, market conditions, and peer performance by analyzing and applying statistical modeling and machine learning to a wealth of customer-level and market-level data. In this high visibility role, you will work on a diverse set of strategic projects based on the top of house priorities leveraging your data science skills. 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 2 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

Nice To Haves

Master’s Degree in “STEM” field (Science, Technology, Engineering, or Mathematics), or PhD in “STEM” field (Science, Technology, Engineering, or Mathematics)
At least 2 years’ experience in Python, Scala, or R
At least 2 years’ experience with machine learning
At least 2 years’ experience with SQL
Possesses the Data Science trifecta of job specific skills: great data expertise, amazing data and software engineering skills, and terrific modeling skills
Be a source of positive energy, inspiration and motivation for the team
Be excited about working in financial services
Be action oriented, always leaning in and moving with a sense of urgency
Multi-task, able to handle multiple priorities in parallel, and extremely well organized
Be self-confident, a self-starter, and a strategic thinker
Build strong, trusted relationships and operate in an influential manner with executives to assist in driving the strategic agenda
Be a strong facilitator, negotiator and mediator, with the ability to drive to consensus
Be highly collaborative (comfortable partnering with all levels of the organization)
Be experienced at seeing patterns and commonalities that can create a bigger picture out of small pieces, anticipating what’s around the corner

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