Senior Associate, Data Scientist - US Card DFS Acquisitions

Capital One • McLean, VA

21h

About The Position

Capital One, a Fortune 200 company and a leader in data-driven decision-making, is seeking a Senior Associate, Data Scientist. The company disrupted the credit card industry in 1988 by personalizing offers using statistical modeling and relational databases. As a Data Scientist, you will join a team leading the next wave of disruption using advanced computing and machine learning technologies across billions of customer records to help customers in their financial lives. The US Card DFS Acquisitions Integration Data Science team builds industry-leading machine learning models for core underwriting decisions in new credit card customer acquisitions. This team ensures models meet risk standards, enables their use in acquisition policies, scales key DFS insights into the COF ecosystem, and develops or refits models combining COF and Discover populations to drive value. The team collaborates with data engineers, platform engineers, product managers, credit, and business analysts, owning the full lifecycle of model development, deployment, monitoring, governance, and ongoing usage. In this role, you will partner with cross-functional teams to deliver customer-centric products, leverage a broad stack of technologies including Python, Conda, AWS, H2O, and Spark to uncover insights from large datasets, build machine learning models from design to implementation, and translate complex technical work into clear 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)
• Experience working with AWS
• 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
• Innovative (continually research and evaluate emerging technologies, stay current on published state-of-the-art methods, technologies, and applications and seek out opportunities to apply them)
• Creative (thrive on bringing definition to big, undefined problems, love asking questions and pushing hard to find answers, not afraid to share a new idea)
• Technical (comfortable with open-source languages, passionate about developing further, hands-on experience developing data science solutions using open-source tools and cloud computing platforms)
• Statistically-minded (built models, validated them, and backtested them, know how to interpret a confusion matrix or a ROC curve, experience with clustering, classification, sentiment analysis, time series, and deep learning)
• A data guru (comfortable with “Big data”, skills to retrieve, combine, and analyze data from a variety of sources and structures, knows understanding the data is often the key to great data science)

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

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Number of Employees: 5,001-10,000 employees