Principal Associate, Data Science - US Card Fraud Authentication Team

Capital One•McLean, NY

22h

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. The US Card Fraud Authentication Data Science team operates at the critical intersection of fraud defenses and customer experience. We leverage a modern tech stack - including Python, AWS, Spark, H2O, and SQL - to analyze complex datasets and optimize authentication decisioning at Capital One. Our mission is to stay ahead of evolving fraud patterns through a combination of rigorous quantitative analysis and the development of scalable machine learning solutions.

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 5 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 3 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)

Nice To Haves

Master’s Degree in “STEM” field (Science, Technology, Engineering, or Mathematics) plus 3 years of experience in data analytics, or PhD in “STEM” field (Science, Technology, Engineering, or Mathematics)
At least 1 year of experience working with AWS
At least 3 years of experience in Python, Scala, or R
At least 3 years of experience with machine learning
At least 3 years of experience with SQL

Responsibilities

Dive into ambiguous fraud landscapes to identify opportunities where machine learning can drive value.
Help design the technical vision for our authentication workflows, evaluating feasibility and prototyping innovative ML solutions.
Harness our big data ecosystem (Spark, AWS, and more) to uncover hidden patterns in massive datasets, turning raw information into actionable fraud-prevention strategies.
Partner with a cross-functional team of business stakeholders, data scientists, software engineers, and product managers to deliver a product customers love.
Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation.
Bridge the gap between technical complexity and business strategy, articulating the "why" behind your solutions to influence stakeholders and achieve tangible business goals.